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Works315 from public data
- Deep Clustering with Convolutional Autoencoders543
A convolutional autoencoders structure is developed to learn embedded features in an end-to-end way and a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment.
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- Scalable Multi-view Subspace Clustering with Unified Anchors362
This work proposes a Scalable Multi-view subspace Clustering with Unified Anchors (SMVSC), which combines anchor learning and graph construction into a unified optimization framework and achieves comparable or better clustering performance much more efficiently.
- Deep Graph Clustering via Dual Correlation Reduction338
This work proposes a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner and introduces a propagation regularization term to enable the network to gain long-distance information with the shallow network structure.
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- Deep Fusion Clustering Network307
A Deep Fusion Clustering Network (DFCN) is proposed, in which an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoen coder for consensus representation learning.
- Multiple Kernel k-means with Incomplete Kernels300
Two simple yet effective algorithms that perform multiple kernel clustering directly with the presence of incomplete kernel matrices, which are treated as auxiliary variables to be jointly optimized and which consistently achieve superior performance are proposed.
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- Multiple Kernel k-Means Clustering with Matrix-Induced Regularization260
This paper proposes an MKKM clustering with a novel, effective matrix-induced regularization to reduce such redundancy and enhance the diversity of the selected kernels and shows that maximizing the kernel alignment for clustering can be viewed as a special case of this approach.
- Multi-view Clustering via Late Fusion Alignment Maximization257
This paper theoretically uncovers the connection between existing k-means clustering and the alignment between base partitions and consensus partition and proposes a simple but effective multi-view algorithm termed MVC-LFA, which proposes to maximally align the consensus partition with the weighted base partitions.
- CGD: Multi-View Clustering via Cross-View Graph Diffusion231
The proposed CGD takes the traditional predefined graph matrices of different views as input, and learns an improved graph for each single view via an iterative cross diffusion process by capturing the underlying manifold geometry structure of original data points, and leveraging the complementary information among multiple graphs.
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- Unsupervised feature selection via multiple graph fusion and feature weight learning190
This paper introduces a novel and effective unsupervised feature selection method via multiple graph fusion and feature weight learning (MGF^2WL) to address these issues, and develops a graph fusion term to fuse multiple predefined similarity graphs for learning a unified similarity graph.
- DealMVC: Dual Contrastive Calibration for Multi-view Clustering189
A novel Dual contrastive calibration network for Multi-View Clustering (DealMVC) is proposed, which first design a fusion mechanism to obtain a global cross-view feature, and proposes a global Contrastive calibration loss to constrain the consistency of pair-wise view features.
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- Deep Incomplete Multi-View Clustering with Cross-View Partial Sample and Prototype Alignment144
A Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep Incomplete Multi-view Clustering and a prototype alignment module to achieve incomplete distribution calibration across views are proposed.
- One-Step Multi-View Clustering With Diverse Representation141
This work proposes a one-step multi-view clustering with diverse representation (OMVCDR) method, which incorporates multi-view learning and k-means into a unified framework and develops an efficient optimization algorithm with proven convergence to solve the resultant problem.
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- Optimal Neighborhood Kernel Clustering with Multiple Kernels133
An optimal neighborhood kernel clustering (ONKC) algorithm is proposed to enhance the representability of the optimal kernel and strengthen the negotiation between kernel learning and clustering and significantly outperforms the state-of-the-art ones in the literature.
- Improved autoencoder for unsupervised anomaly detection128
Different from previous methods, by integrating the anomaly detection‐based loss and autoencoder's reconstruction loss, IAEAD can jointly optimize for anomaly detection tasks and learn representations that preserve the local data structure to avoid feature distortion.
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- Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network120
E^3Outlier for the first time leverages a discriminative DNN for better representation learning, by using surrogate supervision to create multiple pseudo classes from original unlabelled data, and proposes the negative entropy based score as a simple and effective outlierness measure.
- Deep Embedded Clustering with Data Augmentation118
This paper proposes the framework of Deep Embedded Clustering with Data Augmentation (DEC-DA), which first train an autoencoder with the augmented data to construct the initial feature space and constrain the embedded features with a clustering loss to further learn clustering-oriented features.
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- A systematic method for fingerprint ridge orientation estimation and image segmentation111
A scheme for systematically estimating fingerprint ridge orientation and segmenting fingerprint image by means of evaluating the correctness of the ridge orientation based on neural network is proposed and is compared with VeriFinger 4.2 published by Neurotechnologija Ltd. in 2004, and the comparison shows that the proposed scheme leads to an improved accuracy of minutiae detection.
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- Gaussian Mixture Model Clustering with Incomplete Data110
Different from existing approaches that first impute the absence and then perform GMM clustering tasks on the imputed data, this article proposes to integrate the imputation and GMm clustering into a unified learning procedure.
- Multi-view Clustering via Deep Matrix Factorization and Partition Alignment106
This work proposes a novel multi-view clustering algorithm via deep matrix decomposition and partition alignment, and an alternating optimization algorithm is developed to solve the optimization problem with proven convergence.
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- Let the Data Choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-View Clustering101
A novel anchor-based method termed Flexible and Diverse Anchor Graph Fusion for Scalable Multi-view Clustering (FDAGF) and a novel hybrid fusion strategy for multi-size anchor graphs with theoretical proof, which allows flexible and diverse anchor graph fusion.
- Fast Continual Multi-View Clustering With Incomplete Views93
Comprehensive experiments conducted on various datasets demonstrate the superiority of FCMVC-IV over the competing approaches, which maintains a scalable consensus coefficient matrix and updates its knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices.
- CONVERT: Contrastive Graph Clustering with Reliable Augmentation92
A novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (COVERT) that distills reliable semantic information by recovering the perturbed latent embeddings and a label-matching mechanism is designed to guide the model by clustering information through aligning the semantic labels and the selected high-confidence clustering pseudo labels.
- Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix88
This paper designs an efficient algorithm with proved convergence to solve the resultant optimization problem of the optimal Laplacian matrix, and extends it to the late fusion version for accurate and efficient multi-view clustering.
- Efficient and Effective Incomplete Multi-View Clustering87
The proposed Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence.
- Hyperparameter selection of one-class support vector machine by self-adaptive data shifting85
This paper proposes a fully automatic OCSVM hyperparameter selection method, which requires no tuning of additionalhyperparameter, based on a novel self-adaptive “data shifting” mechanism.
- Video anomaly detection and localization by local motion based joint video representation and OCELM85
To model features of normal video events, the newly-emergent one-class Extreme Learning Machine (OCELM) is introduced as the data description algorithm with a tremendous reduction in training time, which makes the approach easier for model updating and more applicable to fast learning from the rapidly generated surveillance data.
- Fingerprint matching based on global alignment of multiple reference minutiae84
A minutia matching method based on global alignment of multiple pairs of reference minutiae, commonly distributed in various fingerprint regions, is proposed, which leads to improvement in system identification performance.
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- K-Means Clustering With Incomplete Data81
A novel K-means based clustering algorithm is proposed which unifies the clustering and imputation into one single objective function and makes these two processes be negotiable with each other to achieve optimality.
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- Multiple kernel clustering with local kernel alignment maximization78
A novel MKC algorithm with a "local" kernel alignment, which only requires that the similarity of a sample to its k-nearest neighbours be aligned with the ideal similarity matrix, which helps the clustering algorithm to focus on closer sample pairs that shall stay together and avoids involving unreliable similarity evaluation for farther sample pairs.
- Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF72
This model describes the whole image as an undirected probabilistic graphical model and was developed using an automatic label-map mechanism for determining nuclear, cytoplasmic and background regions and the proposed gap-search algorithm is much more faster than pixel-based and superpixel-based algorithms.
- Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph70
A novel local sample-weighted MKC (LSWMKC) model is proposed that possesses better local manifold representation and outperforms existing kernel or graph-based clustering algorithms.
- Dual Test-Time Training for Out-of-Distribution Recommender System68
This paper proposes a novel Dual Test-Time-Training framework for OOD Recommendation, termed DT3OR, which is the first work to address OOD recommendation via a test-time-training strategy and conducts experiments on five datasets with various backbones.
- Continual Multi-view Clustering68
This work proposes a continual approach on the basis of late fusion multi-view clustering framework that only needs to maintain a consensus partition matrix and update knowledge with the incoming one of a new data view rather than keep all of them.
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- Interpolation-Based Contrastive Learning for Few-Label Semi-Supervised Learning66
An interpolation-based method to construct more reliable positive sample pairs and a novel contrastive loss to guide the embedding of the learned network to change linearly between samples so as to improve the discriminative capability of the network by enlarging the margin decision boundaries are proposed.
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- Multi-View Spectral Clustering with Optimal Neighborhood Laplacian Matrix62
This paper proposes a novel optimal neighborhood multi-view spectral clustering algorithm, which enhances the representative capacity of the optimal Laplacian and better utilizes the hidden high-order connection information, leading to improved clustering performance.
- Anchor-based multi-view subspace clustering with hierarchical feature descent60
An Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent with Hierarchical Feature Descent with Hierarchical Feature Descent is proposed to tackle the discrepancy among views through hierarchical feature descent and project to a common subspace, which reveals dependency of different views.
- Subspace segmentation-based robust multiple kernel clustering60
This paper unify the constrained kernel polarization and subspace segmentation into a single procedure, where the resultant affinity matrix embedded with robust subspace structural information is utilized to guide the linear combination of base kernels.
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- Sample-Level Cross-View Similarity Learning for Incomplete Multi-View Clustering58
A Sample-level Cross-view Similarity Learning (SCSL) method for Incomplete Multi-view Clustering that simultaneously construct a complete similarity matrix across views based on the inter-view sample relationship and the intra-view sample relationship and a simultaneously learning consensus representation ensures the validity of the projection.
- Localized Incomplete Multiple Kernel k-means57
Different from existing MKKM-IK, LI-MKKM only requires the similarity of a sample to its k-nearest neighbors to align with their ideal similarity values, which helps the clustering algorithm to focus on closer sample pairs that shall stay together and avoids involving unreliable similarity evaluation for farther sample pairs.
- One-Stage Incomplete Multi-view Clustering via Late Fusion54
This paper proposes to unify the imputation of incomplete views and the clustering task into a single optimization procedure, so that the learning of the consensus partition matrix can directly assist the final clusteringtask.
- Scalable Incomplete Multi-View Clustering with Structure Alignment53
A novel incomplete anchor graph learning framework termed Scalable Incomplete Multi-View Clustering with Structure Alignment (SIMVC-SA), which constructs the view-specific anchor graph to capture the complementary information from different views and proposes a novel structure alignment module to refine the cross-view anchor correspondence.
- Initializing Then Refining: A Simple Graph Attribute Imputation Network53
A novel graph-oriented imputation framework called initializing then refining (ITR), where the structure information is employed for initial imputation, and the affinity structure is adaptively updated through iterations by calculating the sample-wise correlations upon the recomposed embeddings.
- Detecting Abnormality without Knowing Normality51
A novel unsupervised approach that not only avoids manually specifying normality for training as supervised methods do, but also takes the whole spatio-temporal context into consideration, which can further exclude abnormal events and enhance abnormality detection performance.
- Walking to singular points of fingerprints51
A fast algorithm for detecting singular points by walking directly to them instead of scanning the image, which can be combined with state-of-the-art method to improve performance.
- Graph regularized spatial–spectral subspace clustering for hyperspectral band selection47
A graph regularized spatial-spectral subspace clustering method for hyperspectral band selection, which adopts superpixel segmentation to preserve the spatial information of HSIs by segmenting their first principal component into diverse homogeneous regions, and which can mitigate the effect of noise on the band selection.
- Mixed Graph Contrastive Network for Semi-supervised Node Classification45
This work proposes a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN), which improves the discriminative capability of the latent embeddings by an interpolation-based augmentation strategy and a correlation reduction mechanism.
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- Robust Graph-Based Multi-View Clustering45
This paper defines a min-max formulation for robust learning and rewrite it as a convex and differentiable objective function whose convexity and differentiability are carefully proved and can efficiently solve the resultant problem using a reduced gradient descent algorithm, and the corresponding solution is guaranteed to be globally optimal.
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- A Non-parametric Graph Clustering Framework for Multi-View Data43
This article is devoted to getting rid of hyper-parameters, and devise a non-parametric graph clustering (NpGC) framework to more practically partition multi-view data.
- Two steps for fingerprint segmentation43
Two steps for fingerprint segmentation are proposed to exclude the remaining ridge region from the foreground, and the effectiveness of the proposed method in segmenting the remaining ridges as background and in turn producing much less spurious minutiae than the existing method is shown.
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- An Efficient and Robust Unsupervised Anomaly Detection Method Using Ensemble Random Projection in Surveillance Videos40
This paper proposes a novel efficient three-stage unsupervised anomaly detection method that embraces random projection instead of autoencoder or its variants in previous works and achieves comparable performance contrast with the state-of-the-art unsuper supervised method with much less running time.
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- Fingerprint Enhancement Using Circular Gabor Filter37
This paper follows Hong's Gabor filter based enhancement scheme but uses a circle support filter and tunes the filter’s frequency and size differently and does improve the performance of minutiae detection.
- Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering36
A hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly, to validate the superiority of the proposed method when compared with other state-of-the-art ones.
- GraphLearner: Graph Node Clustering with Fully Learnable Augmentation33
GraphLearner introduces learnable augmentors to generate high-quality and task-specific augmented samples for CDGC and introduces an adversarial learning mechanism within the method to refine the high-confidence matrices.
- Tensor-Based Multi-View Block-Diagonal Structure Diffusion for Clustering Incomplete Multi-View Data32
- DVSAI: Diverse View-Shared Anchors Based Incomplete Multi-View Clustering30
This work designs a novel framework called Diverse View-Shared Anchors based Incomplete multi-view clustering (DVSAI), and devise an integration scheme with linear computational and memory expenditures to integrate the outputted multi-scale unified anchor graphs such that running spectral algorithm generates the spectral embedding.
- Automatic and Aligned Anchor Learning Strategy for Multi-View Clustering30
This work introduces a Hierarchical Bipartite Neighbor Clustering (HBNC) strategy to adaptively select a suitable number of representative anchors in each view and proposes an innovative anchor alignment strategy in response to the varying quantities of anchors across different views.
- An Incremental Feature Learning Algorithm Based on Least Square Support Vector Machine30
An incremental feature learning algorithm based on Least Square Support Vector Machine is proposed, which is more suitable to deal with classification tasks which can not be well solved by using a single kernel function.
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- R²MRF: Defocus Blur Detection via Recurrently Refining Multi-Scale Residual Features29
A deep neural network which Recurrently Refines Multi-scale Residual Features (R2MRF) for defocus blur detection consistently and significantly outperforms the competitors in terms of both efficiency and accuracy.
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- Triangle Lasso for Simultaneous Clustering and Optimization in Graph Datasets29
This paper demonstrates through extensive numerical experiments that triangle lasso is robust to the imperfect data, and develops an efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) to obtain a moderately accurate solution.
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- Efficient Anchor Learning-based Multi-view Clustering -- A Late Fusion Method28
The proposed Efficient Anchor Learning-based Multi-view Clustering (AL-MVC) algorithm achieves the state-of-the-art performance in both clustering performance and efficiency.
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- Semantic instance segmentation with discriminative deep supervision for medical images27
A proposal-free segmentation network with discriminative deep supervision (DDS), which at the same time allows us to gain the power of the proposal-based method, and robust pixel-wise instance-level cues are introduced for semantic segmentation.
- Large-scale k-means clustering via variance reduction27
This paper proposes a position correction mechanism to correct the drift of the cluster centers, and proposes a variance reduced k-means named VRKM, which outperform the state-of-the-art method and obtain about 2 × and 4 × speedups for large-scale clustering, respectively.
- DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System26
This work proves that directly aligning the representations of LLMs and collaborative models is suboptimal for enhancing downstream recommendation tasks performance, based on the information theorem, and proposes a novel plug-and-play alignment framework for LLMs and collaborative models.
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- Efficient Multiple Kernel k-Means Clustering With Late Fusion25
This work designs two multiple kernel k-means clustering algorithms with late fusion, whose computational complexities linearly grow with the number of samples, and integrally optimize the various clustering matrices into the optimal consensus clustering results iteratively.
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- Contrastive Continual Multiview Clustering With Filtered Structural Fusion22
A data buffer to store filtered structural information is developed and utilized to guide the generation of a robust partition matrix via contrastive learning and a sampling strategy called clustering then sample is proposed to address the high complexity involved in acquiring and storing structural information.
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- E3 Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection22
This paper proposes a novel framework named E 3 Outlier that can perform effective and end-to-end deep outlier removal and introduces self-supervision into deep OD, and argues that inlier priority, a property that encourages DNN to prioritize inliers duringSelf-supervised learning, makes it possible to perform end- to-end OD.
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- Fusion Multiple Kernel K-means20
A novel algorithm is proposed, termed as Fusion Multiple Kernel k-means (FMKKM), which unifies base partition learning and late fusion clustering into one single objective function, and adopts early fusion technique to capture more sufficient information in kernel matrices.
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- Adaptive partial graph learning and fusion for incomplete multi‐view clustering19
This study presents a novel incomplete multi‐view clustering method via adaptive partial graph learning and fusion (APGLF), which can capture the local data structure of both within‐view and cross‐view as well as recover the optimal cluster structure of original data.
- DMP-ELMs: Data and model parallel extreme learning machines for large-scale learning tasks19
The goal is to build scalable ELMs with a large number of samples and hidden neurons, parallel running on clusters without computational and memory bottlenecks while having the same output results as the sequential ELM.
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- Deep Embedding for Determining the Number of Clusters18
Deep Embedding Determination first combines the virtues of the convolutional autoencoder and the t-SNE technique to extract low dimensional embedded features and determines the number of clusters using an improved density-based clustering algorithm.
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- Integrative Multi‐Omics and Routine Blood Analysis Using Deep Learning: Cost‐Effective Early Prediction of Chronic Disease Risks17
This work highlights the role of multiomics analysis in the prediction of chronic disease risk, and the development and validation of predictive models based on blood routine results can help advance personalized medicine and reduce the cost of disease screening in the community.
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- Understand Dynamic Regret with Switching Cost for Online Decision Making16
A new theoretical analysis framework is provided that shows an interesting observation; that is, the relation between the switching cost and the dynamic regret is different for settings of OA and OCO.
- Affine Equivariant Autoencoder16
An affine equivariant autoencoder is proposed to learn features that areEquivariant to the affine transformation in an unsupervised manner to fill the gap in learning transformation invariant features in deep neural networks.
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- Multiple Kernel Clustering With Global and Local Structure Alignment15
A global and local structure alignment framework for MKC (GLSAMKC) is proposed which well considers both the alignment between the global structure andLocal structure of data with the same ideal similarity matrix.
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- Max-Mahalanobis Anchors Guidance for Multi-View Clustering14
A novel method called Max-Mahalanobis Anchors Guidance for multi-view Clustering (MAGIC), which guides the cross-view representations to progressively align with well-defined anchors, significantly enhancing the performance of multi-view clustering.
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- Sample Weighted Multiple Kernel K-means via Min-Max optimization13
A novel sample weighted multiple kernel k-means via min-max optimization (SWMKKM), which sufficiently considers the sum of relationship between one sample and the others to represent the sample weights, helps clustering algorithm pay more attention to samples with more positive effects on clustering and avoids unreliable overestimation for samples with poor quality.
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- View Gap Matters: Cross-view Topology and Information Decoupling for Multi-view Clustering12
A Tree-Based View-Gap Maintaining Multi-View Clustering (TGM-MVC) method, which introduces a novel conceptualization of multiple views as a graph structure, and derives the minimum spanning tree of the views, reflecting the neighbouring relationships among them.
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- Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios11
A novel multi-view clustering framework for the automatic identification and rectification of noisy data, termed AIRMVC is proposed, which reformulate noisy identification as an anomaly identification problem using GMM and introduces a noise-robust contrastive mechanism to generate reliable representations.
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- Consensus Multiple Kernel K-Means Clustering With Late Fusion Alignment and Matrix-Induced Regularization10
The correlations between different clustering partitions are calculated as a matrix-induced regularization to encourage the diversity of clustering results and the proposed algorithm outperforms the existing state-of-the-art multi-kernel methods in clustering performance with less time complexity.
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- Test-Time Training on Graphs with Large Language Models (LLMs)9
This paper designs a novel Test-Time Training pipeline, LLMTTT, which conducts the test-time adaptation under the annotations by LLMs on a carefully-selected node set and introduces a hybrid active node selection strategy that considers not only node diversity and representativeness, but also prediction signals from the pre-trained model.
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- Deep Incomplete Multi-View Clustering with Distribution Dual-Consistency Recovery Guidance8
This work proposes BURG, a novel method for incomplete multi-view clustering with distriBution dUalconsistency $\boldsymbol{R}$ ecovery $\boldsymbol{G}$ uidance, which treats each sample as a distinct category and performs cross-view distribution transfer to predict the distribution space of missing views.
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- Simultaneous Clustering and Optimization for Evolving Datasets7
This paper proposes a new variant of the alternating direction method of multipliers (ADMM) to solve the problem of evolving datasets efficiently and extensive empirical studies confirm the effectiveness of this method.
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- Late Fusion Multi-view Clustering via Global and Local Alignment Maximization6
This paper reveals the theoretical connection of existing k-means clustering and the alignment between base partitions and the consensus one, and proposes a simple but effective multi-view algorithm termed LF-MVC-GAM, which optimally fuses multiple source information in partition level from each individual view and maximally aligns the consensus partition with these weighted base ones.
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- Robustness Can Be Cheap: A Highly Efficient Approach to Discover Outliers under High Outlier Ratios6
A Low-rank based Efficient Outlier Detection (LEOD) framework to achieve favorable robustness against high outlier ratios with much cheaper computations to encourage satisfying robustness with low computational cost later is proposed.
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- Generalized Deep Multi-View Clustering Via Causal Learning With Partially Aligned Cross-View Correspondence5
This paper is the first work to deal generalized multi-view clustering via causal learning via causal learning, and designs a Variational Auto-Encoder for causal learning by incorporating an encoder from existing information to estimate the invariant features.
- ProtoRefine: Enhancing Prototypes with Similar Structure in Few-Shot Learning5
This work introduces ProtoRefine, which employs the structural information of categories within the base domain that bear relevance to the new tasks, generating additional sample embeddings that refines the prototype representation, thus providing a more accurate prototype for category classification.
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- Learning the Anchors with Similar Distributions to Original Data for Multi-view Clustering4
This paper designs a Multi-View OT (MVOT) framework through complementary and consensus representation learning, and establishes the strong dual condition for MVOT by the relative interior, and successfully obtains the transport plan between anchors and original data for each view within linear computational complexity.
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- Scalable k-means for large-scale clustering4
This paper proposes two scalable k -means algorithms, Scalable Lloyd’s k-means and Scalable Mini-Batch k-means, distributed extensions of Lloyd’s algorithm and the mini-batch k -means, respectively that use the data-parallel technique to scale beyond computational and memory limits of a single machine.
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- Multiple kernel clustering with late fusion consensus local graph preserving3
This paper proposes a late fusion MKC method with local graph refinement that unifies the traditional weighted multiple kernel k‐means, kernel partition, and graph construction into a single optimization procedure and enjoys more flexible kernel representations, leading to significant clustering improvements.
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- Multiple Kernel Clustering Based on Self-Weighted Local Kernel Alignment3
A multiple kernel clustering algorithm based on self-weighted local kernel alignment, which can learn a proper weight to clustering performance for each local kernel alignment, is designed and tested.
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- Variance Reduced K-Means Clustering2
A variance reduced k-mean VRKM is proposed, which outperforms the state-of-the-art method, and can be obtained 4× speedup for large-scale clustering.
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- Object Affordance Recognition and Grounding via Multi-scale Cross-modal Representation Learning1
A novel approach is proposed that learns an affordance-aware 3D representation and employs a stage-wise inference strategy leveraging the dependency between grounding and classification tasks, enabling inference of full potential affordance areas at a suitable regional scale.
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- A framework for tuberculosis drug resistance classification based on deep transfer learning1
A transfer learning framework to classify the tuberculosis CT images which is provided in ImageCLEF 2017 challenge is adopted by firstly sampling 9 slices from 3D CT volumes and then training a deep transfer network based on VGG16, and finally voting to label the images.
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- On the Selection of Multi Optimal Imaging Frames in Single Time Slot for Earth Observation Satellite1
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- ANIMATE: Unsupervised Attributed Graph Anomaly Detection with Masked Graph Transformers–
Graph Transformers (GTs) are introduced into UGAD task, termed as unsupervised attributed graph Anomaly detectioN wIth Masked grAph TransformErs (ANIMATE), and can capture graph information that preserves the distinguishable characteristics of abnormalities from a global perspective.
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- Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discovery–
This paper proposes a novel framework named Intra-view and Interview Correlation Guided Multi-view Novel Class Discovery (IICMVNCD), which is the first attempt to explore NCD in multi-view setting so far and results validate the effectiveness of the proposed approach.
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- A Theoretical Revisit to Linear Convergence for Saddle Point Problems–
Convex-concave bilinear Saddle Point Problems (SPP) is widely used in lasso problems, Support Vector Machines, game theory, and so on.
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- A Kernel Method with Manifold Regularization for Interactive Segmentation–
A kernel based semi-supervised learning framework with manifold regularization for interactive image segmentation with extensive experimental results demonstrate the effectiveness of the proposed approach.
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- 一种基于交点权重图的指纹焦点检测方法 (Fingerprint Focal Point Detection Method Based on Map of Cross Points' Weights).–
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- A Max-Margin Learning Algorithm with Additional Features–
This paper shows how to use a max-margin learning framework to classify the incomplete data directly without any completing of the missing features, and develops an objective function which aims to maximize the margin of each sample in its own relevant subspace.
- A Large Margin Classifier with Additional Features–
It is shown how incomplete data can be classified directly without completing any missing features in a large-margin learning framework by avoiding the pre-processing phase in which the data is completed and the algorithm can achieve better or comparable classification accuracy compared to the existing algorithms.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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