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- Wanqing LiSuggested from co-authorship
- Zhanjie SongSuggested from co-authorship
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Works231 from public data
- 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.
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- RGB-D-based action recognition datasets: A survey291
A comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi- view datasets, and 7 multi-person datasets, resulting in a number of recommendations for collection of new datasets and use of evaluation protocols.
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- 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.
- A network traffic forecasting method based on SA optimized ARIMA–BP neural network221
A network traffic prediction method based on SA (Simulated Annealing) optimized ARIMA, non-linear model BPNN and optimization algorithm SA can fully realize the potential of mining linear and non- linear laws of historical network traffic data, hence improving the prediction accuracy.
- GCFAgg: Global and Cross-View Feature Aggregation for Multi-View Clustering218
The consensus data presentation from multiple views is obtained via cross-sample and cross-view feature aggregation, which fully explores the complementary of similar samples, and the consensus representation and the view-specific representation are aligned by the structure-guided contrastive learning module.
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- Unsupervised feature selection via multiple graph fusion and feature weight learning191
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.
- Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks184
The image-based representations enable the existing ConvNet models trained on image data without training a large number of parameters from scratch, and achieve the state-of-art results on three large datasets, namely, the large-scale continuous gesture recognition dataset.
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- Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks157
A new representation, namely, Scene Flow to Action Map (SFAM), that describes several long term spatio-temporal dynamics for action recognition from RGB-D data and takes better advantage of the trained ConvNets models over ImageNet.
- Unsupervised feature selection via latent representation learning and manifold regularization144
A robust unsupervised feature selection method which embeds the latent representation learning into feature selection and is carried out in the learned latent representation space which is more robust to noises.
- Robust unsupervised feature selection via dual self-representation and manifold regularization138
Experimental results on ten real-world data sets demonstrate that the proposed method can effectively identify important features, outperforming many state-of-the-art unsupervised feature selection methods in terms of clustering accuracy (ACC) and normalized mutual information (NMI).
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- Multispectral Object Detection via Cross-Modal Conflict-Aware Learning131
This paper develops a two-stream one-stage detector based on CALNet for multispectral object detection and demonstrates that the approach considerably outperforms existing methods in resolving the cross-modal semantic conflict issue and achieving state-of-the-art accuracy in detection results.
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- ConvNets-Based Action Recognition from Depth Maps through Virtual Cameras and Pseudocoloring112
Through transferring learning the models originally trained over ImageNet for image classification, the three ConvNets are trained independently on the color-coded DMMs constructed in three orthogonal planes, achieving state-of-the-art results on these datasets.
- Consensus learning guided multi-view unsupervised feature selection107
A consensus learning guided multi-view unsupervised feature selection method, which embeds multi-View feature selection into a non-negative matrix factorization based clustering with sparse constrain.
- Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices106
This paper proposes an open framework to use the kernel matrix over feature dimensions as a generic representation and discusses its properties and advantages, which significantly elevates covariance representation to the unlimited opportunities provided by this new representation.
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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