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- Deep Learning for Generic Object Detection: A Survey2,890
A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
- Improved Deep Embedded Clustering with Local Structure Preservation1,085
The Improved Deep Embedded Clustering (IDEC) algorithm can jointly optimize cluster labels assignment and learn features that are suitable for clustering with local structure preservation by integrating the clustering loss and autoencoder’s reconstruction loss.
- 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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- Late Fusion Incomplete Multi-View Clustering374
This work proposes Late Fusion Incomplete Multi-view Clustering (LF-IMVC) which effectively and efficiently integrates the incomplete clustering matrices generated by incomplete views and develops a three-step iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence.
- 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.
- A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal354
A first survey for knowledge graph reasoning tracing from static to temporal and then to multi-modal KGs, and points out the challenges and potential opportunities to enlighten the readers.
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- 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 Kernels306
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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- Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors269
This work proposes a scalable and parameter-free MVSC method to directly output the clustering labels with optimal anchor graph, termed as Efficient One-pass Multi-view Subspace Clustering with Consensus Anchors (EOMSC-CA), which combines anchor learning and graph construction into a uniform framework to boost clustering performance.
- Global and Local Structure Preservation for Feature Selection263
This paper proposes a global and local structure preservation framework for feature selection (GLSPFS) which integrates both global pairwise sample similarity and local geometric data structure to conduct feature selection and shows that the best feature selection performance is always obtained when the two factors are appropriately integrated.
- 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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- Simple Contrastive Graph Clustering209
A simple contrastive graph clustering algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function, and a novel cross-view structural consistency objective function is designed to enhance the discriminative capability of the learned network.
- Multiple kernel extreme learning machine204
A general learning framework, termed multiple kernel extreme learning machines (MK-ELM), to address the lack of a general framework for ELM to integrate multiple heterogeneous data sources for classification and can achieve comparable or even better classification performance than state-of-the-art MKL algorithms, while incurring much less computational cost.
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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 2026-10-12. 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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