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Multi‐view intrinsic low‐rank representation for robust face recognition and clustering
Author(s) -
Wang Zhiyang,
Abhadiomhen Stanley Ebhohimhen,
Liu Zhifeng,
Shen Xiangjun,
Gao Wenyun,
Li Shuying
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12232
Subject(s) - cluster analysis , representation (politics) , rank (graph theory) , pattern recognition (psychology) , computer science , artificial intelligence , facial recognition system , face (sociological concept) , sparse approximation , data mining , machine learning , mathematics , social science , combinatorics , politics , sociology , political science , law
In the last years, subspace‐based multi‐viewface recognition has attracted increasing attention and many related methods have been proposed. However, the most existing methods ignore the specific local structure of different views. This drawback can cause these methods' discriminating ability to degrade when many noisy samples exist in data. To tackle this problem, a multi‐view low‐rank representation method is proposed, which exploits both intrinsic relationships and specific local structures of different views simultaneously. It is achieved by hierarchical Bayesian methods that constrain the low‐rank representation of each view so that it matches a linear combination of an intrinsic representation matrix and a specific representation matrix to obtain common and specific characteristics of different views. The intrinsic representation matrix holds the consensus information between views, and the specific representation matrices indicate the diversity among views. Furthermore, the model injects a clustering structure into the low‐rank representation. This approach allows for adaptive adjustment of the clustering structure while pursuing the optimization of the low‐rank representation. Hence, the model can well capture both the relationship between data and the clustering structure explicitly. Extensive experiments on several datasets demonstrated the effectiveness of the proposed method compared to similar state‐of‐the‐art methods in classification and clustering.

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