Scaled Simplex Representation for Subspace Clustering

The self-expressive property of data points, that is, each data point can be linearly represented by the other data points in the same subspace, has proven effective in leading subspace clustering (SC) methods. Most self-expressive methods usually construct a feasible affinity matrix from a coeffici...

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Vydané v:	IEEE transactions on cybernetics Ročník 51; číslo 3; s. 1493 - 1505
Hlavní autori:	Xu, Jun, Yu, Mengyang, Shao, Ling, Zuo, Wangmeng, Meng, Deyu, Zhang, Lei, Zhang, David
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Affinity Algorithms Clustering Clustering algorithms Coefficients Computational modeling Constraints Correlation Data models Data points Iterative methods Mathematical analysis Matrix methods Optimization Representations Scaled simplex representation (SSR) self-expressiveness Statistical analysis subspace clustering (SC) Subspaces
ISSN:	2168-2267, 2168-2275, 2168-2275
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