The DOI matches its Crossref record; 55 of the 56 references it cites were found in a registry or an index.
Multi-view unsupervised feature selection with tensor robust principal component analysis and consensus graph learning
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The continuous-time representation learning module is constructed through cascaded ODE blocks that parameterize the hidden state evolution as smooth vector fields, enabling adaptive depth computation and gradient-stable learning dynamics that are intrinsically resistant to abrupt parameter shifts during task transitions35.”
Citations
Cites
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691
2008 IEEE Conference on Computer Vision and Pattern Recognition
(2008) · doi:10.1109/cvpr12618.2008 · Cited by 4,263
Robust Recovery of Subspace Structures by Low-Rank Representation
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y. H., & Ma, Y. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.88 · Cited by 3,585
Proceedings of IEEE International Conference on Computer Vision
(1995) · doi:10.1109/iccv.1995 · Cited by 2,339
Advances in Neural Information Processing Systems 18
Murray, I., Mackay, D., Zoubin Ghahramani, A., & Skilling, J. (2006) · Cited by 1,927
Laplacian Score for Feature Selection
He, X., Cai, D., & Niyogi, P. (2005) · Cited by 1,763
Graph Convolutional Networks for Hyperspectral Image Classification
Hong, D., Gao, L., Yao, J. T., Zhang, B., Plaza, A., & Chanussot, J. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3015157 · Cited by 1,720
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
Macskassy, S., & USA, K. '. T. 1. A. S. I. C. O. K. D. A. D. M. 2. S. D. C. (2014) · doi:10.1145/2623330 · Cited by 1,546
More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification
Hong, D., Gao, L., Yokoya, N., Yao, J. T., Chanussot, J., Du, Q., & Zhang, B. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3016820 · Cited by 1,394
Research works printed but not linked: 1
- Li, 2012, Unsupervised feature selection using nonnegative spectral analysis, vol. 26, 1026
Cited by
Sorted by citations
A review on multi-view learning
Yu, Z., Dong, Z., Yu, C., Yang, K., Fan, Z., & Chen, C. L. P. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40004-w · Cited by 104
Structure learning with consensus label information for multi-view unsupervised feature selection
Cao, Z., & Xie, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121893 · Cited by 70
Semi-supervised multi-view feature selection with adaptive similarity fusion and learning
Jiang, B., Liu, J., Wang, Z., Zhang, C., Yang, J., Wang, Y., Sheng, W., & Ding, W. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111159 · Cited by 62
Multi-view unsupervised feature selection with consensus partition and diverse graph
Cao, Z., Xie, X., & Li, Y. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120178 · Cited by 55
Multi-view unsupervised complementary feature selection with multi-order similarity learning
Cao, Z., & Xie, X. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111172 · Cited by 48
Xue, Y., & Zhang, C. (2024) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2024.101760 · Cited by 45
Zhang, H., Arif, M., Thafar, M. A., Albaradei, S., Cai, P., Zhang, Y., Tang, H., & Lin, H. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1529335 · Cited by 38
Self-citationZhong, J., Shang, R., Xu, S., & Li, Y. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110453 · Cited by 25
Xu, S., Xie, X., & Cao, Z. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113043 · Cited by 23
Collaborative and Discriminative Subspace Learning for unsupervised multi-view feature selection
Wu, J.-S., Li, Y., Gong, J.-X., & Min, W. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108145 · Cited by 21
Partition-Level Tensor Learning-Based Multiview Unsupervised Feature Selection
Cao, Z., & Xie, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3482440 · Cited by 21
Tensor schatten-p norm guided incomplete multi-view self-representation clustering
Liu, W., Jing, X., Zeng, D., & Zhang, T. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111733 · Cited by 14
Symmetrical Self-Representation and Data-Grouping Strategy for Unsupervised Feature Selection
Yuan, A., You, M., Wang, Y., Li, X., & Li, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3437364 · Cited by 13
Low-rank tensor based smooth representation learning for multi-view unsupervised feature selection
Wang, C., Song, P., Duan, M., Zhou, S., & Cheng, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112902 · Cited by 13
Wang, D., Wang, L., Chen, W., Wang, H., & Liang, C. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110041 · Cited by 13
Self-citationLatent Semantics and Anchor Graph Multi-Layer Learning for Multi-View Unsupervised Feature Selection
Liu, Q., Liu, S., Liu, X., & Dai, J. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3591515 · Cited by 12
Wu, J.-S., Gong, J.-X., Liu, J., Huang, W., & Zheng, W. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120604 · Cited by 10
Low-rank sparse fully-connected tensor network for tensor completion
Yu, J., Li, Z., Ma, G., Wang, J., Zou, T., & Zhou, G. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111000 · Cited by 9
High-order correlation preserved multi-view unsupervised feature selection
Duan, M., Song, P., Zhou, S., Cheng, Y., Mu, J., & Zheng, W. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109507 · Cited by 8
Multi-view Stable Feature Selection with Adaptive Optimization of View Weights
Cui, M., Wang, K., Ding, X., Xu, Z., Wang, X., & Shi, P. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111970 · Cited by 7
Adaptively robust high-order tensor factorization for low-rank tensor reconstruction
Song, Z., Chen, Y., & Zhao, W. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111600 · Cited by 7
Multiview feature selection combining latent space and similarity structure learning
Li, Z., Chen, H., Yin, T., Yuan, Z., Luo, C., Horng, S., & Li, T. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111874 · Cited by 7
Unsupervised feature selection based on dual-graph clustering learning and adaptive weighting
Wang, X., Shang, R., Liu, C., Li, Y., & Xu, S. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113401 · Cited by 7
CFCN: An HLA-peptide Prediction Model based on Taylor Extension Theory and Multi-view Learning
Rao, B., Han, B., Wei, L., Zhang, Z., Jiang, X., & Manavalan, B. (2024) · Current Bioinformatics · doi:10.2174/0115748936299044240202100019 · Cited by 7
Diversity and consistency graph learning guided multi-view unsupervised feature selection
Xu, H., Xu, D., & Zhang, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113455 · Cited by 6
Feature selection-based rough graph classification via graph convolutional neural networks
Sun, L., Cai, Y., Ding, W., & Xu, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111502 · Cited by 6
Duan, M., Song, P., Zhou, S., Mu, J., & Liu, Z. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104668 · Cited by 6
Automatic ensemble feature selection for multi-view data
Ding, X., Cui, M., & Wang, K. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130437 · Cited by 6
shabani, M., Kadoch, M., & Mirjalili, S. (2025) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2024.109231 · Cited by 5
Dong, X., Wu, D., Ke, Y., & Nie, F. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103511 · Cited by 4
Wu, J.-S., Yu, J.-T., Wu, J.-Y., Min, W., & Zheng, W.-S. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112047 · Cited by 4
Bai, T., Xie, J., Liu, Y., & Liu, B. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3483997 · Cited by 5
Multi-view Unsupervised Feature Selection via Global and Local Kernelized Graph Learning
Xu, M., Xie, X., Li, Y., & Chao, G. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130786 · Cited by 3
Wang, D., Huang, Y., Ying, W., Bai, H., Gong, N., Wang, X., Dong, S., Zhe, T., Liu, K., Xiao, M., et al. (2026) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3801742 · Cited by 3
Robust multi-view unsupervised feature selection via latent consensus structure learning
Wang, C., & Song, P. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122177 · Cited by 3
Robust high-order graph learning for incomplete multi-view clustering
Wang, D., Ren, F., Zhuang, Y., & Liang, C. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127580 · Cited by 3
Self-citationYang, F., Zheng, B., Zhao, R., & Liu, G. (2025) · Numerical Linear Algebra with Applications · doi:10.1002/nla.70013 · Cited by 2
Zhang, H., Lin, X., Wang, Y., Pei, W.-F., Ma, G., Zhou, Z. H., Deng, K., Yan, D., & Liu, T. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf249 · Cited by 2
Self-citationYuan, Y., Wu, W., Xu, C., Zhang, W., & Jin, C. (2025) · Fractal and Fractional · doi:10.3390/fractalfract9110717 · Cited by 2
Multi-view unsupervised feature selection with unified measurement of consistency and diversity
Xu, S., Xie, X., Chao, G., & Xiong, Y.-J. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112728 · Cited by 2
Yang, H., Shi, C., Xin, Y., Cai, J., Zhou, L., Su, M., He, Y., Zhao, X., & Xun, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132307 · Cited by 2
Multi-view semi-supervised feature selection with multi-order similarity and tensor learning
Chen, H., Xie, X., & Xiong, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131573 · Cited by 2
Li, G., Cao, Z., Liang, C., Xiao, Q., & Luo, J. (2024) · Current Bioinformatics · doi:10.2174/0115748936288616240212073805 · Cited by 2
Self-citationMulti-view semi-supervised feature selection based on adaptive graph and tensor learning
Chen, H., Xie, X., & Chao, G. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06637-5 · Cited by 1
An incomplete multiview clustering approach considering missing data recovery based on consistency
Li, Z., Chen, H., Xiang, B., Yuan, Z., Luo, C., Horng, S., & Li, T. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107836 · Cited by 1
Variable priority for unsupervised variable selection
Zhou, L., Lü, M., & Ishwaran, H. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112727 · Cited by 1
Liu, Q., Liu, S., Dai, J., Zhu, X., & Liu, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3635015 · Cited by 1
Adaptive Topological Similarity Learning for Incomplete Multi-View Unsupervised Feature Selection
Xu, L., Li, K., Wang, D., Zhou, F., Lv, F., Li, T., & Huang, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3642753 · Cited by 1
High-order multi-view unsupervised feature selection based on inter-view and intra-view
Xie, X., Xu, J., Xu, S., Xu, Y., & Xiong, Y. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114444 · Cited by 1
Kernelized linear principal component discriminant analysis
Qu, L., & Pei, Y. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.108539 · Cited by 1
Xie, W., Zhang, L.-H., Ling, C., & He, H. (2026) · Journal of Scientific Computing · doi:10.1007/s10915-026-03205-y · Cited by 1
Zhang, Y., Huang, M., Gao, W., Sun, Z., Liu, S., Han, B., Zhao, L., Sun, W., Yan, Y., & Liu, Y. (2026) · Spectrochimica Acta Part B Atomic Spectroscopy · doi:10.1016/j.sab.2026.107522 · Cited by 1
Mi, Y., Chen, H., Yuan, Z., Sang, B., Luo, C., & Li, T. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132417 · Cited by 1
Wang, X., Wang, M., Cui, H. N., & Zhang, Y. (2024) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3696409.3700255 · Cited by 1
Wu, J.-S., Yu, H.-W., Li, Y., & Min, W. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06406-4 · Cited by 1
Zhou, S., & Li, Q. (2025) · Scientific Reports · doi:10.1038/s41598-025-31685-9
Wang, D., Wang, L., Chen, W., Wang, H., & Liang, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4772554
Self-citationJoint Multi-view unsupervised feature selection based on tensor learning
Xu, Y., Xie, X., Jin, C., Chao, G., & Li, Y. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115458
Cheng, R., Sun, Z., Qi, K., Wu, W., & Xu, L. (2026) · Biomimetics · doi:10.3390/biomimetics11050312
Shi, T.-L., (6656), L. W., You, Z.-H., Huang, Y., Wei, M., & Li, Z.-W. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.152978
Cao, C., Li, M., Guo, M., Wang, C., Zou, Q., & Niu, M. (2026) · BMC Biology · doi:10.1186/s12915-026-02656-x
Cai, Y., Dai, F., Zhang, Z., & Zhang, K. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01017-6
Simultaneous local and global clustering guided multi-view unsupervised feature selection
Xu, D., Xu, H., Sun, Z., Wu, C., Zhang, Y., & Qiao, L. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.116122
Li, Z., Huang, Z., Li, Y., Zhao, C., & Yu, L. (2026) · Genome Research · doi:10.1101/gr.282246.126
iBitter-HF: A Method for Bitter Peptide Sequence Identification Based on Hybrid Feature Embedding
Yan, F., Xiang, S., Yi, T., Kuang, Z., Liu, H., Luo, X., & Lv, Z. (2026) · Foods · doi:10.3390/foods15173016
Gravitation and Causality Guided Multi-View Unsupervised Feature Selection
Yang, J., Yu, K., Cao, F., Wang, X., Yang, S., & Liang, J. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3730695
Jiang, J., Xie, X., Li, Y., Chao, G., & Xiong, Y. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135017
DECANT: decoupling mechanism from context in single-cell drug perturbation representation
Qi, R., Teng, W., Yang, X., Cheng, Y., Shaytan, A. К., & Liu, B. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag662
Cross-entropy based on t-SVD of third order tensor: Applications in object detection
Kharazmi, O., & Beik, F. P. A. (2026) · Applied Mathematical Modelling · doi:10.1016/j.apm.2026.117318
Shi, H. W., Lin, Y., Liu, D., Wu, H., Isa, A. N. M., Wei, L., Zou, Q., & Chen, X. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag530
Kang, X., Li, J., Lam, C.-T., Zou, Q., Lin, C., & Wei, L. (2026) · ACS Synthetic Biology · doi:10.1021/acssynbio.6c00522
pLM-HP: Peptide hormone prediction using pre-trained protein language model representations
Ao, C., Jiao, S., Su, X., & Yang, H. (2026) · Journal of Advanced Research · doi:10.1016/j.jare.2026.10.002
Metadata
- Authors
- Liang, C., Wang, L., Liu, L., Zhang, H., & Guo, F.
- Affiliations
- Shandong Normal University (CN)
- Central South University (CN)
- Corresponding author
- Guo, Fei
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 141
- Pages
- 109632
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W4366778915
- Cited by
- 72
- References
- 56
- Citations by year
- 2026: 21 · 2025: 29 · 2024: 20 · 2023: 2
- FWCI
- 6.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Pattern Recognition (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Mathematics › Computational Mathematics › Tensor decomposition and applications
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- tensor robust principal component analysis, inter-view correlation, low-rank tensor, pseudo-labeling, alternating direction method of multipliers, noise robustness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review