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- Kernel Adaptive Filtering: A Comprehensive Introduction638
Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces and addresses the principal bottleneck of kernel adaptive filterstheir growing structure.
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- Kernel Affine Projection Algorithms193
KAPA inherits the simplicity and online nature of KLMS while reducing its gradient noise, boosting performance and provides a unifying model for several neural network techniques, including kernel least-mean-square algorithms, kernel adaline, sliding-window kernel recursive-least squares, and regularization networks.
- Canonical correlation analysis networks for two-view image recognition123
A canonical correlation analysis network (CCANet) is presented to address image classification, in which images are represented by two-view features, and the experimental results demonstrate that the CCANet algorithm is more effective than PCANet, RandNet-1 andRandNet-2.
- On a PCA-based lung motion model122
The goal of this work is to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling and propose a new method to derive the entire lung motion using a single internal marker based on the PCA model.
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- Hessian Regularized Support Vector Machines for Mobile Image Annotation on the Cloud108
A scheme for image annotation on the cloud is presented, which transmits mobile images compressed by Hamming compressed sensing to the cloud and conducts semantic annotation through a novel Hessian regularized support vector machine on thecloud.
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- Multiview dimension reduction via Hessian multiset canonical correlations100
HesMCC is presented, which takes the advantage of Hessian and provides superior extrapolating capability and finally leverage the performance of TCCA, KMUDA, MCCA and LapMCC for multiview dimension reduction.
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- Detecting structural damage under unknown seismic excitation by deep convolutional neural network with wavelet-based transmissibility data86
A new approach is proposed for detecting structural damage subject to unknown seismic excitation based on a convolutional neural network with wavelet-based transmissibility of structural response data using the ability in subtle information acquisition of wavelet transform.
- Multiview Hessian regularized logistic regression for action recognition78
This paper carefully conduct extensive experiments on the unstructured social activity attribute (USAA) dataset and the experimental results demonstrate the effectiveness of the proposed multiview Hessian regularized logistic regression for human action recognition.
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- A coordinated optimization framework for long-term complementary operation of a large-scale hydro-photovoltaic hybrid system: Nonlinear modeling, multi-objective optimization and robust decision-making63
A coordinated optimization framework for the long-term complementary operation of large-scale hydro-PV hybrid systems is established that simultaneously optimizes the economic benefit and operational safety of the hybrid system and a novel robust decision-making model is developed to evaluate, rank and select the Pareto optimal solutions.
- A multi-objective risk management model for real-time flood control optimal operation of a parallel reservoir system63
The results show that the developed multi-objective risk management model can provide operation schedules that satisfy flood control objectives and simultaneously minimize the overall risks.
- HesGCN: Hessian graph convolutional networks for semi-supervised classification62
HesGCN can learn more efficient data features by fusing the original feature information with its structure information based on Hessian, and gets a more efficient convolution layer rule by optimizing the one-order spectral graph Hessian convolutions.
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- Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition61
A new unsupervised feature fusion method termed multiview Cauchy estimator feature embedding (MCEFE) for human action recognition is proposed, which integrates the encoded complementary information in multiple views to find the unified data representation and the projection matrices.
- HSAE: A Hessian regularized sparse auto-encoders60
This paper incorporates both Hessian regularization and sparsity constraints into auto-encoders and proposed HSAE outperforms the related baseline algorithms and efficiently extracts the hidden structure in the data by using sparsity constraint.
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- Semi-supervised classification by graph p-Laplacian convolutional networks55
This paper exploits the manifold structure information of data by the graph p -Laplacian matrix and proposes the graph p -Laplacian convolutional networks (GpLCN), which can extract more abundant sample features and improves the classification performance utilizing graph p -Laplacian to preserve the rich intrinsic data manifold structure information.
- Texture analysis and classification of ultrasound liver images55
In this study, GLGCM (Gray Level Gradient Co-Occurrence Matrix) was implemented for texture analysis of ultrasound liver images first, followed by the use of GLCM (Gray Level Co-occurrence Matrix) at the second stage for classification.
- Targeted Attention Attack on Deep Learning Models in Road Sign Recognition52
Experimental results validate that the TAA method improves the attack successful rate and reduces the perturbation loss compared with the popular RP2 method, and also provides good properties, e.g., transferability and generalization capability.
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- Kernel least mean square algorithm with constrained growth52
A new efficient methodology for constraining the increase in length of a radial basis function (RBF) network resulting from the kernel LMS algorithm without significant sacrifice on performance is proposed.
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- Human activity recognition by manifold regularization based dynamic graph convolutional networks47
A manifold regularized dynamic graph convolutional network (MRDGCN) that automatically updates the structure information by manifold regularization until model fitting and builds an optimization convolution layer formulation to acquire the optimal structure information.
- Reinforcement online learning for emotion prediction by using physiological signals46
A reinforcement online learning (ROL) method for real-time emotion state prediction by exploiting the reward to modify the predictor during the online training iterations is proposed and gains both significant time reduction and prominent performance.
- Road Detection by Using a Generalized Hough Transform46
The experimental results demonstrate that the proposed generalized Hough transform implementation for road detection in remote sensing images is superior to the traditional algorithms in terms of accuracy and computing complexity.
- MDFM: Multi-Decision Fusing Model for Few-Shot Learning43
A novel method Multi-Decision Fusing Model (MDFM) is proposed, which comprehensively considers the decisions based on multiple FEMs to enhance the efficacy and robustness of the model.
- Answer Questions with Right Image Regions: A Visual Attention Regularization Approach42
A novel visual attention regularization approach, namely, AttReg, which can be integrated into most visual attention-based VQA models and require no human attention supervision, and empirically validate such property of visual attention and compare it with the prevalent gradient-based approaches.
- Egocentric Early Action Prediction via Adversarial Knowledge Distillation42
This article proposes a novel multi-modal adversarial knowledge distillation framework that seamlessly integrate adversarial learning with latent and discriminative knowledge regularizations encouraging the learned representations of the partial video to be more informative and discrim inative toward the action prediction.
- Where Does the Performance Improvement Come From?39
The reproducibility of image-text retrieval models is analyzed and the examination of the elements that lead to improved performance by pretrained and nonpretrained models in retrieving images and text are examined.
- Unified Cross-domain Classification via Geometric and Statistical Adaptations36
UCGS models the divergence minimization and classifier construction in a unified way based on structural risk minimization principle and coupled adaptations theory and constructs an adaptive model by simultaneously minimizing the structural risk on labelled source data.
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- Essential oils of Zingiber officinale: Chemical composition, in vivo alleviation effects on TPA induced ear swelling in mice and in vitro bioactivities32
The essential oils of Z. officinale exhibited anti-inflammatory effects on TPA induced ear swelling in mice, antibacterial effects on different bacteria, anti-oxidant effect on DPPH free radical scavenging and anti-tumor property on human lung cancer A549 and human prostate cell lines LNCaP.
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- Label embedded dictionary learning for image classification31
A label embedded dictionary learning (LEDL) method to utilise the L1-norm as the sparse regularization term so that the hard-to-optimize problem by solving the convex optimization problem is avoided.
- Manifold regularized kernel logistic regression for web image annotation31
Manifold regularized kernel logistic regression (KLR) is proposed for web image annotation and has the following advantages: (1) the KLR has a smooth loss function; (2) theKLR produces an explicit estimate of the probability instead of class label; and (3)The KLR can naturally be generalized to the multi-class case.
- LMAE: A large margin Auto-Encoders for classification30
The Large Margin Auto-Encoders (LMAE) is proposed to further boost the discriminability by enforcing different class samples to be large marginally distributed in hidden feature space and demonstrates that the proposed LMAE outperforms the traditional Auto-encoders algorithm.
- Full passive UHF RFID Tag with an ultra-low power, small area, high resolution temperature sensor suitable for environment monitoring30
An UHF RFID Tag with an ultra-low power, small area, high resolution temperature sensor which adopted double voltage-controlled oscillators (VCO) has been designed and implemented using the SMIC CMOS 0.18@mm EEPROM 2P4M process.
- Confronting the Carbon-Footprint Challenge of Blockchain29
The study sheds light on the urgency of developing the PoS mechanism to solve the current sustainability problem of blockchain and comprehensively elucidate the current and projected energy consumption and carbon footprint of the PoW- and PoS-based Bitcoin and Ethereum blockchain platforms.
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- Dynamic and Intelligent Modeling Methods for Joint Operation of a Flood Control System29
This paper proposes a novel method of alleviating the “curse of dimensionality” by reducing the number of dimensions in multireservoir systems by up to 50%.
- GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning28
A novel label prediction method, Isolated Graph Learning (IGL), which can weaken the negative influence of noise from the feature representation perspective, and is also flexible to independently complete training and testing procedures, which is suitable for SSFSL.
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- Intelligent identification of effective reservoirs based on the random forest classification model25
The results indicate that the RFC model has the characteristic of high classification accuracy, low sensitivity to flood samples and high stability in the dynamic identification of effective reservoirs compared to other models.
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- Saliency-Guided Remote Sensing Image Super-Resolution24
A saliency-guided remote sensing image super-resolution (SG-GAN) method to alleviate the above issue while maintaining the merits of GAN-based methods for the generation of perceptual-pleasant details.
- Color Transfer Pulse-Coupled Neural Networks for Underwater Robotic Visual Systems24
In this scheme, pulse-coupled neural network (PCNN)-based image enhancement and color transfer algorithms are combined to enhance the underwater image to avoid color imbalance and enhance details while suppressing noise.
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- Multi-scale region selection network in deep features for full-field mammogram classification22
A deep multi-scale region selection network (MRSN) in deep features for end-to-end training to classify full-field mammography without ROI or segmentation annotation is proposed, Inspired by the idea of multi-example learning and the patch classifier.
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- Example-feature graph convolutional networks for semi-supervised classification22
The definition of the spectral example-feature graph (EFG) convolution that simultaneously utilizes the example graph Laplacian and feature graph LaPLacian to better preserve the local geometry distributions of data is introduced.
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- Exploring differences between left and right hand motor imagery via spatio-temporal EEG microstate22
Electroencephalography microstate method was used to process the EEG-based motor imagery to obtain microstate and indicates that the microstate can be a promising feature to improve the performance of the brain-computer interface classification.
- Hessian-Regularized Co-Training for Social Activity Recognition22
The proposed Hessian-regularized co-training improves the generalizability of a classifier, especially when there are a small number of labeled examples and a large number of unlabeled examples, and outperforms baseline methods, including the traditional co- training and LapCo algorithms.
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- Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity21
The results demonstrate that the predicted intrahemispheric structural connections and the weights distribution are highly consistent with the empirical SC derived from diffusion magnetic resonance imaging (dMRI) and probabilistic tractography, thus strongly supporting the model and algorithms proposed.
- Stochastic multi-criteria decision making based on stepwise weight information for real-time reservoir operation21
The results indicate that the framework helps decision makers deal with stochastic MCDM problems with greater confidence and less psychological pressure, and enables a risk-informed decision can be made with higher reliabilities.
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- BESA: BERT-based Simulated Annealing for Adversarial Text Attacks20
This research uses the BERT Masked Language Model to generate contextual-aware candidate words to produce fluent adversarial text and avoid grammar errors and proposes BESA, a BERT-based Simulated Annealing algorithm to address these two problems.
- Ensemble p-Laplacian Regularization for Scene Image Recognition20
An ensemble p-Laplacian regularization (EpLapR) is developed to fully approximate the intrinsic manifold of the data distribution and construct a fused graph by introducing an optimization approach to assign suitable weights on different p value graphs.
- Two‐order graph convolutional networks for semi‐supervised classification20
The authors deduce and simplify the formula of two-order spectral graph convolutions to preserve rich local information and build a layerwise GCN based on this two- order approximation, i.e. two-Order GCN (TGCN) for semi-supervised classification.
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- Semantic-Preserving Adversarial Text Attacks19
A Bigram and Unigram-based adaptive adaptive Semantic Preservation Optimization (BU-SPO) approach which attacks text documents not only at a unigram word level but also at a bigram level to avoid generating meaningless sentences is devised.
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- Urban Perception: Sensing Cities via a Deep Interactive Multi-task Learning Framework18
By conducting extensive experiments over a public large-scale benchmark dataset, it is demonstrated that the proposed DRAMA scheme outperforms several state-of-the-art baselines and is released to facilitate other researches.
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- Vega-MT: The JD Explore Academy Translation System for WMT2217
The JD Explore Academy’s submission of the WMT 2022 shared general translation task is described, which pushes the limit of bidirectional training for translation by scaling up two main factors, i.e. language pairs and model sizes.
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- Support vector machine active learning by Hessian regularization17
A semi-supervised algorithm is introduced to learn the classifier and then an active learning scheme is performed on top of the semi- supervised scheme to achieve a better performance than the traditional active learning methods.
- Hessian Regularized Sparse Coding for Human Action Recognition17
HessianSC significantly outperforms LaplacianSC and the traditional sparse coding algorithm for action recognition and is presented as a fast iterative shrinkage-thresholding algorithm (FISTA) for HessianSC.
- Enhanced online CAM: Single-stage weakly supervised semantic segmentation via collaborative guidance16
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- Hazy Re-ID: An Interference Suppression Model for Domain Adaptation Person Re-Identification Under Inclement Weather Condition15
A teacher-student model is used in the ISM to distill the interference information at the feature level by reducing the discrepancy between the clear and the hazy intrinsic similarity matrix.
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- Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation14
This work proposes a Domain-invariant Graph Learning (DGL) approach for domain adaptation with only a few labeled source samples and introduces the Nyström method to construct a plastic graph that shares similar geometric property with the target domain.
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- Interference Distillation for Underwater Fish Recognition14
The teacher-student model is used to distill the interference in underwater fish images, which intends to classify the fish species more efficiently and outperforms the competitors.
- PaintNet: A shape-constrained generative framework for generating clothing from fashion model14
Inspired by painting, a two-stage shape-constrained clothing generative framework is proposed, dubbed as PaintNet, which is devised to predict the intermediate shape map for the to-be-generated clothing item based on the latent representation learning.
- Class specific or shared? A cascaded dictionary learning framework for image classification14
A novel framework named cascaded dictionary learning framework (CDLF) is proposed to combine the specific dictionary learning with shared dictionary learning to describe the feature to boost the performance of classification sufficiently.
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- Prompt-Learning for Cross-Lingual Relation Extraction13
This paper designs and implements several prompt templates, including hard, soft, and hybrid prompts, and empirically test their performance on competitive multilingual PLMs, specifically mBART, to demonstrate that the Prompt-Xre algorithm significantly outperforms both vanilla mult bilingual PLMs and other existing models, achieving state-of-the-art performance in XRE.
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- A Super Fast Algorithm for Estimating Sample Entropy13
A super fast algorithm to estimate sample entropy based on Monte Carlo is proposed, with computational costs independent of N (the length of the time series) and the estimation converging to the exact sample entropy as the number of repeating experiments becomes large.
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- A general framework for co-training and its applications13
This paper proposes a general framework for co- Training according to the diverse learners constructed in co-training, and provides three types of co- training implementations, including co-trained on multiple views, co-Training on multiple classifiers, and co-train on multiple manifolds.
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- Hessian Regularized Distance Metric Learning for People Re-Identification11
The encouraging results suggest that manifold regularization can boost distance metric learning and the proposed Hessian regularized distance metric learning algorithm outperforms the traditional manifold regularized distance metric learning algorithms including graph Laplacian regularization algorithm.
- Back‐dropout transfer learning for action recognition11
A transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back- dropout strategy on them to penalize errors.
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- Cross-Dimension Attention Guided Self-Supervised Remote Sensing Single-Image Super-Resolution9
This paper proposes a cross-dimension attention guided self-supervised remote sensing single-image super-resolution method (CASSISR), which does not require pre-training on a dataset, only utilizes the internal information reproducibility of a single image, and uses the lower-resolution image downsampled from the input image to train the cross- dimension attention network (CDAN).
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- MWLN: Multilevel Wavelet Learning Network for Continuous-Scale Remote-Sensing Image Super-Resolution8
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- MKEL: Multiple Kernel Ensemble Learning via Unified Ensemble Loss for Image Classification8
A novel ensemble model, called Multiple Kernel Ensemble Learning (MKEL), is developed by introducing a unified ensemble loss.
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- Building accurate translation-tailored large language models with language-aware instruction tuning7
A two-stage fine-tuning algorithm to improve the instruction-following ability of translation-tailored LLMs, particularly for maintaining accurate translation directions, and shows that it can preserve the model’s performance on other tasks, such as supervised translation and general tasks.
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- Domain Adaptation with Few Labeled Source Samples by Graph Regularization5
Experimental results validate that the proposed Graph Regularized Domain Adaptation (GDA) outperforms the traditional algorithms when there are few labeled source samples and can reduce source and target distribution divergency to learn a generalized classifier.
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- Effective human action recognition by combining manifold regularization and pairwise constraints5
A novel local structure preserving approach by considering both manifold regularization and pairwise constraints is introduced and can better preserve the local geometry of data distribution and achieve the effective recognition of human action recognition.
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- Noise-Robust Few-Shot Classification via Variational Adversarial Data Augmentation3
Applying VADA for training can cause the Few-Shot classification model more robust when dealing with noisy data, while not losing the generalization ability.
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- Lesion Asymmetry Screening Assisted Global Awareness Multi-View Network for Mammogram Classification3
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- Hessian regularization by patch alignment framework2
This paper constructs a Hessian using the patch alignment framework and applies it to regression problems and demonstrates the superiority of the proposed solution in human face classification.
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- Edge Loss for Remote Sensing Image Super-Resolution1
An edge-based dense connection generative adversarial network (SREDGAN), which minimizes the edge differences between the generated image and its corresponding ground truth, which improves on NWPU-VHR-10 and UCAS-AOD datasets.
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- Cauchy Estimator Discriminant Learning for RGB-D Sensor-based Scene Classification1
Experiments with the frequently used NYU Depth V1 dataset suggest the effectiveness of CEDL compared with other state-of-the-art scene classification methods.
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- Behavior Recognition Based on Multimodal Information of Unmanned Aerial Vehicle Swarm of DeepSeek V3–
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- Reasoning Like Experts: Leveraging Multimodal Large Language Models for Drawing-based Psychoanalysis–
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- GSA4FDA: Deep Geometric and Statistic Alignment for Fewer Labeled Domain Adaptation–
This work proposes a novel method called Deep Geometric and Statistic Alignment for Fewer labeled Domain Adaptation (GSA4FDA), which achieves the target task with fewer labeled source samples by combining manifold learning and leveraging the local geometric structure of sufficient unlabeled source samples.
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- Sparse principle motion component for one-shot gesture recognition–
Experimental results show that the proposed sparse PCA based principle motion component (SPMC) method for one-shot gesture recognition can improve the accuracy of gesture recognition.
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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 2026-10-16. 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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