The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Multi-view Subspace Clustering
Open accessAbstract
For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. The proposed method performs clustering on the subspace representation of each view simultaneously. Meanwhile, we propose to use a common cluster structure to guarantee the consistence among different views. In addition, an efficient algorithm is proposed to solve the problem. Experiments on four benchmark data sets have been performed to validate our proposed method. The promising results demonstrate the effectiveness of our method.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Belkin, M., & Niyogi, P. (2002) · Advances in Neural Information Processing Systems 14 · doi:10.7551/mitpress/1120.003.0080 · Cited by 4,412
Robust Recovery of Subspace Structures by Low-Rank Representation
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., & Ma, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.88 · Cited by 3,594
Sparse Subspace Clustering: Algorithm, Theory, and Applications
Elhamifar, E., & Vidal, R. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.57 · Cited by 3,106
Fei-Fei, L., Fergus, R., & Perona, P. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.09.012 · Cited by 2,359
Mixtures of Probabilistic Principal Component Analyzers
Tipping, M. E., & Bishop, C. M. (1999) · Neural Computation · doi:10.1162/089976699300016728 · Cited by 1,873
Co-regularized Multi-view Spectral Clustering
Kumar, A., Rai, P., & Daumé, H. (2011) · Cited by 1,093
Analyzing appearance and contour based methods for object categorization
Leibe, B., & Schiele, B. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211497 · Cited by 787
Cited by
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
Dizaji, K. G., Herandi, A., Deng, C., Cai, W., & Huang, H. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.612 · Cited by 579
Self-citationGraph Learning for Multiview Clustering
Zhan, K., Zhang, C., Guan, J., & Wang, J. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2751646 · Cited by 543
Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance
Wang, S., Liu, X., Zhu, X., Zhang, P., Zhang, Y., Gao, F., & Zhu, E. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3131941 · Cited by 517
One-Step Multi-View Spectral Clustering
Zhu, X., Zhang, S., He, W., Hu, R., Lei, C., & Zhu, P. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2873378 · Cited by 265
Deep Attributed Network Embedding
Gao, H., & Huang, H. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/467 · Cited by 257
Self-citation
Metadata
- Authors
- Gao, H., Nie, F., Li, X., & Huang, H.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 4238-4246
- OpenAlex
- W2199534117
- Cited by
- 614
- References
- 24
- FWCI
- 13.64
- Type
- conference paper
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Multi-view Subspace Clustering. https://app.cochat.ai/papers/10.1109/iccv.2015.482
- The paper
- Gao, H., Nie, F., Li, X., & Huang, H. (2015). Multi-view Subspace Clustering. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.482
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
