Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization

Multiview data clustering attracts more attention than their single-view counterparts due to the fact that leveraging multiple independent and complementary information from multiview feature spaces outperforms the single one. Multiview spectral clustering aims at yielding the data partition agreeme...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2018-10, Vol.29 (10), p.4833-4843
Hauptverfasser:	Wang, Yang, Wu, Lin, Lin, Xuemin, Gao, Junbin
Format:	Artikel
Sprache:	eng
Schlagworte:	Agreements Australia Clustering Correlation Data correlation Data processing Divergence Eigenvalues Eigenvectors Iterative multiview clustering agreement Learning systems Linear programming low-rank matrix factorization low-rank representation (LRR) Manifolds Matrix decomposition multiview spectral clustering Objective function Optimization Representations Spectra Strategy Symmetric matrices
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