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- Trusted Multi-View Classification With Dynamic Evidential Fusion558
The variational Dirichlet is introduced to characterize the distribution of the class probabilities, parameterized with evidence from different views and integrated with the Dempster-Shafer theory, which induces accurate uncertainty and accordingly endows the model with both reliability and robustness against possible noise or corruption.
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- Consistent and Specific Multi-View Subspace Clustering450
A novel multi-view subspace clustering method (CSMSC), where consistency and specificity are jointly exploited for subspace representation learning, and forms the multi-View self-representation property using a shared consistent representation and a set of specific representations, which better fits the real-world datasets.
- Deep Partial Multi-View Learning338
A novel framework termed Cross Partial Multi-View Networks (CPM-Nets) is proposed, which aims to fully and flexibly take advantage of multiple partial views to provide completeness and versatility for multi-view representation and theoretically prove the versatility of the learned latent representations.
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- Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning254
This study proposes to conduct the diagnosis of COVID-19 with a series of features extracted from CT images, and shows that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.
- Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion252
A measure to evaluate the reliability of depth map, and use it to reduce the influence of poor depth map on saliency detection, and two saliency maps are integrated into a final saliency map through weighted-sum method according to their importance.
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- Subspace clustering guided unsupervised feature selection209
Experimental results on benchmark datasets for unsupervised feature selection show that SCUFS outperforms the state-of-the-art UFS methods and can uncover the underlying multi-subspace structure of data.
- Provable Dynamic Fusion for Low-Quality Multimodal Data202
A novel multimodal fusion framework termed Quality-aware Multimodal Fusion (QMF) is proposed, which can improve the performance in terms of classification accuracy and model robustness and several uncertainty estimation solutions are naturally available to achieve robust multi-modal fusion.
- CPM-Nets: Cross Partial Multi-View Networks189
This work proposes a novel framework termed Cross Partial Multi-View Networks (CPM-Nets), which gives a formal definition of completeness and versatility for multi-view representation and theoretically proves the versatility of the latent representation learned from this algorithm.
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- Multi-view subspace clustering with intactness-aware similarity185
A novel multi-view subspace clustering model that attempts to form an informative intactness-aware similarity based on the intact space learning technique is proposed and its superior performance over other state-of-the-art alternatives is revealed.
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- Flexible Multi-View Representation Learning for Subspace Clustering174
This paper proposes to conduct subspace clustering based on Flexible Multi-view Representation (FMR) learning, which avoids using partial information for data reconstruction, and achieves superior clustering performance over other state-ofthe-art methods.
- Multi-label feature selection with missing labels172
This paper presents a novel multi-label feature selection model under the circumstance of missing labels, where the most discriminative features are selected and missing labels are recovered simultaneously.
- Multi-view Spectral Clustering Network156
To deeply cluster multi-view data, MvSCN incorporates the local invariance within every single view and the consistency across different views into a novel objective function, where theLocal invariance is defined by a deep metric learning network rather than the Euclidean distance adopted by traditional approaches.
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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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