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Robust Multiview Spectral Clustering via Cooperative Manifold and Low Rank Representation Induced by Zhiyong Xu; Sirui Tian; Stanley Ebhohimhen Abhadiomhen; Xiang-Jun Shen is a Computer Science article available to read on EtoBox.

What is Robust Multiview Spectral Clustering via Cooperative Manifold and Low Rank Representation Induced about?

This paper proposes a novel multiview low-rank clustering method to learn robust multiview clustering from two different data structures, unlike existing methods' one data structure learning technique. Specifically, by inducing the multiview data's manifold through dual data structures to obtain each view's low-rank representation and a similarity matrix, respectively, the proposed method can avoid the uncertainty of one-way manifold learning and obtain optimal clustering performance. To facilitate our approach, each view's low-rank representation is constrained to be a linear mixture of consensus and view-specific parts. In this way, the consensus part and the similarity matrix are then allowed to guide each other to find the more optimal solution adaptively. Besides, we avoid the extra computational time involved in a similarity matrix's spectral post-processing such that our clustering structure is revealed directly through the obtained similarity matrix with the help of a rank constraint. Several experiments were conducted on WebKB, ORL, UCI digits, 3sources, and F-MNIST benchmark datasets to evaluate the effectiveness of the proposed method. Experimental results obtained on al

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Author

Zhiyong Xu; Sirui Tian; Stanley Ebhohimhen Abhadiomhen; Xiang-Jun Shen

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)