Multiview Subspace Clustering by an Enhanced Tensor Nuclear Norm

Despite the promising preliminary results, tensor-singular value decomposition (t-SVD)-based multiview subspace is incapable of dealing with real problems, such as noise and illumination changes. The major reason is that tensor-nuclear norm minimization (TNNM) used in t-SVD regularizes each singular...

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Vydáno v:IEEE transactions on cybernetics Ročník 52; číslo 9; s. 8962 - 8975
Hlavní autoři: Xia, Wei, Zhang, Xiangdong, Gao, Quanxue, Shu, Xiaochuang, Han, Jungong, Gao, Xinbo
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Clustering
Clustering algorithms
Clustering methods
Coefficients
Correlation
Learning
Mathematical analysis
Matrix decomposition
Minimization
Multiview clustering
Optimization
Periodic structures
Singular value decomposition
spectral clustering
Subspaces
tensor-singular value decomposition (t-SVD)
Tensors
weighted nuclear norm
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:Despite the promising preliminary results, tensor-singular value decomposition (t-SVD)-based multiview subspace is incapable of dealing with real problems, such as noise and illumination changes. The major reason is that tensor-nuclear norm minimization (TNNM) used in t-SVD regularizes each singular value equally, which does not make sense in matrix completion and coefficient matrix learning. In this case, the singular values represent different perspectives and should be treated differently. To well exploit the significant difference between singular values, we study the weighted tensor Schatten <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula>-norm based on t-SVD and develop an efficient algorithm to solve the weighted tensor Schatten <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula>-norm minimization (WTSNM) problem. After that, applying WTSNM to learn the coefficient matrix in multiview subspace clustering, we present a novel multiview clustering method by integrating coefficient matrix learning and spectral clustering into a unified framework. The learned coefficient matrix well exploits both the cluster structure and high-order information embedded in multiview views. The extensive experiments indicate the efficiency of our method in six metrics.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2021.3052352