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
Normalized Mutual Information Feature Selection
Estévez, P. A., Tesmer, M., Perez, C. A., & Żurada, J. M. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005601 · Cited by 1,293
Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm
Lu, C., Feng, J., Chen, Y., Liu, W., Lin, Z., & Yan, S. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2891760 · Cited by 1,153
Clustering and projected clustering with adaptive neighbors
Nie, F., Wang, X., & Huang, H. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623726 · Cited by 1,059
On a Theorem of Weyl Concerning Eigenvalues of Linear Transformations I
Fan, K. (1949) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.35.11.652 · Cited by 1,045
Spectral feature selection for supervised and unsupervised learning
Zhao, Z., & Liu, H. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273641 · Cited by 954
Novel Methods for Multilinear Data Completion and De-noising Based on Tensor-SVD
Zhang, Z., Ely, G., Aeron, S., Hao, N., & Kilmer, M. E. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.485 · Cited by 841
Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition
Xia, R., Pan, Y., Du, L., & Yin, J. P. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8950 · Cited by 759
LOCUS: learning object classes with unsupervised segmentation
Winn, J. M., & Jojić, N. (2005) · doi:10.1109/iccv.2005.148 · Cited by 523
On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization
Xie, Y., Tao, D., Zhang, W., Liu, Y., Zhang, L., & Qu, Y. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1086-2 · Cited by 518
A Survey on semi-supervised feature selection methods
Sheikhpour, R., Sarram, M. A., Gharaghani, S., & Chahooki, M. A. Z. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.003 · Cited by 507
Essential Tensor Learning for Multi-View Spectral Clustering
Wu, J., Lin, Z., & Zha, H. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2916740 · Cited by 391
Unsupervised Feature Selection with Structured Graph Optimization
Nie, F., Zhu, W. D., & Li, X. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10168 · Cited by 345
A new simplex sparse learning model to measure data similarity for clustering
Huang, J., Nie, F., & Huang, H. (2015) · International Conference on Artificial Intelligence · Cited by 276
Proceedings of the 2014 SIAM International Conference on Data Mining
(2014) · doi:10.1137/1.9781611973440 · Cited by 234
Auto-weighted multi-view clustering via deep matrix decomposition
Huang, S., Kang, Z., & Xu, Z. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107015 · Cited by 216
Tang, C., Zheng, X., Liu, X., Zhang, W., Zhang, J., Xiong, J., & Wang, L. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3048678 · Cited by 216
Self-citationLearning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection
Wang, M., Wang, Q., Hong, D., Roy, S. K., & Chanussot, J. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3175771 · Cited by 206
The Twist Tensor Nuclear Norm for Video Completion
Hu, W., Tao, D., Zhang, W., Xie, Y., & Yang, Y. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2611525 · Cited by 205
Multi-View Unsupervised Feature Selection with Adaptive Similarity and View Weight
Hou, C., Nie, F., Tao, H., & Yi, D. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2681670 · Cited by 201
Subspace learning for unsupervised feature selection via matrix factorization
Wang, S., Pedrycz, W., Zhu, Q., & Zhu, W. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.08.004 · Cited by 193
Handwritten digit recognition by combined classifiers
van Breukelen, M., Duin, R. P. W., Tax, D. M. J., & den Hartog, J. E. (1998) · Czech digital mathematics library · Cited by 184
Learning semantic visual vocabularies using diffusion distance
Liu, J., Yang, Y., & Shah, M. A. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206845 · Cited by 176
Semi-supervised multi-label learning by constrained non-negative matrix factorization
Liu, Y., Jin, R., & Yang, L. (2006) · Cited by 173
A new emotion database: considerations, sources and scope
Douglas‐Cowie, E., Cowie, R., & Schroeder, N. V. (2000) · Cited by 151
Adaptive Unsupervised Multi-view Feature Selection for Visual Concept Recognition
Feng, Y., Xiao, J., Zhuang, Y., & Liu, X. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37331-2_26 · Cited by 136
Sparse Coding Guided Spatiotemporal Feature Learning for Abnormal Event Detection in Large Videos
Chu, W., Xue, H., Yao, C., & Cai, D. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2846411 · Cited by 132
Feature selection with kernelized multi-class support vector machine
Guo, Y., Zhang, Z., & Tang, F. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107988 · Cited by 130
Unsupervised Feature Selection for Multi-View Data in Social Media
Tang, J., Hu, X., Gao, H., & Liu, H. (2013) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972832.30 · Cited by 128
Supervised Feature Selection With Orthogonal Regression and Feature Weighting
Wu, X., Xu, X., Liu, J., Wang, H., Hu, B. X., & Nie, F. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2991336 · Cited by 121
Multi-label feature selection via manifold regularization and dependence maximization
Huang, R., & Wu, Z. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108149 · Cited by 120
Robust Principal Component Analysis on Graphs
Shahid, N., Kalofolias, V., Bresson, X., Bronstein, M. M., & Vandergheynst, P. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.322 · Cited by 112
Adaptive multi-view feature selection for human motion retrieval
Wang, Z., Feng, Y., Qi, T., Yang, X., & Zhang, J. (2014) · Signal Processing · doi:10.1016/j.sigpro.2014.11.015 · Cited by 102
Multilevel projections with adaptive neighbor graph for unsupervised multi-view feature selection
Zhang, H., Wu, D., Nie, F., Wang, R., & Li, X. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.12.007 · Cited by 91
Self-citationAdaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection
Dong, X. M., Zhu, L., Song, X., Li, J., & Cheng, Z. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/285 · Cited by 86
Guo, X. (2015) · International Conference on Artificial Intelligence · Cited by 85
Multi-view feature selection via Nonnegative Structured Graph Learning
Bai, X., Zhu, L., Liang, C., Li, J., Nie, X., & Chang, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.044 · Cited by 85
Self-citationLocal discriminative based sparse subspace learning for feature selection
Shang, R., Yang, M., Wang, W., Shang, F., & Jiao, L. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.03.026 · Cited by 75
Multi-view unsupervised feature selection with tensor low-rank minimization
Yuan, H., Li, J., Liang, Y., & Tang, Y. Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.02.005 · Cited by 73
Graph regularized locally linear embedding for unsupervised feature selection
Miao, J., Yang, T., Sun, L., Fei, X., Niu, L., & Shi, Y. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108299 · Cited by 70
Unsupervised soft-label feature selection
Wang, F., Zhu, L., Li, J., Chen, H., & Zhang, H. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106847 · Cited by 55
Self-citationDSTPCA: Double-Sparse Constrained Tensor Principal Component Analysis Method for Feature Selection
Hu, Y., Liu, J., Gao, Y.-L., & Shang, J. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2943459 · Cited by 25
Autoweighted Multiview Feature Selection With Graph Optimization
Wang, Q., Xu, J., Chen, M., & Li, X. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3094843 · Cited by 21
Discriminative feature selection with directional outliers correcting for data classification
Yuan, L., Yang, G., Xu, Q., & Lü, T. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108541 · Cited by 13
Zhao, Y., Jiao, C.-N., Wang, M., Liu, J., Wang, J., & Zheng, C.-H. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00441-8 · Cited by 10
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
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