The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Smooth Representation Clustering
Abstract
Subspace clustering is a powerful technology for clustering data according to the underlying subspaces. Representation based methods are the most popular subspace clustering approach in recent years. In this paper, we analyze the grouping effect of representation based methods in depth. In particular, we introduce the enforced grouping effect conditions, which greatly facilitate the analysis of grouping effect. We further find that grouping effect is important for subspace clustering, which should be explicitly enforced in the data self-representation model, rather than implicitly implied by the model as in some prior work. Based on our analysis, we propose the SMooth Representation (SMR) model. We also propose a new affinity measure based on the grouping effect, which proves to be much more effective than the commonly used one. As a result, our SMR significantly outperforms the state-of-the-art ones on benchmark datasets.
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Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,502
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A tutorial on spectral clustering
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From few to many: illumination cone models for face recognition under variable lighting and pose
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Locality Preserving Projections
He, X., & Niyogi, P. (2003) · Cited by 4,119
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
Self-citationSparse 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
Shape and motion from image streams under orthography: a factorization method
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Cited by
Incomplete multi-modal representation learning for Alzheimer’s disease diagnosis
Liu, Y., Fan, L., Zhang, C., Zhou, T., Xiao, Z., Geng, L., & Shen, D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101953 · Cited by 102
Metadata
- Authors
- Hu, H., Lin, Z., Feng, J., & Zhou, J.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 3834-3841
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2088025572
- Cited by
- 286
- References
- 21
- FWCI
- 13.64
- Type
- conference paper
- Language
- en
- Open access
- closed
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