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Multiview spectral clustering via complementary information
Author(s) -
Ma Shuangxun,
Liu Yuehu,
Zheng Qinghai,
Li Yaochen,
Cui Zhichao
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5701
Subject(s) - cluster analysis , spectral clustering , computer science , benchmark (surveying) , artificial intelligence , pattern recognition (psychology) , similarity (geometry) , biclustering , lagrange multiplier , correlation clustering , cure data clustering algorithm , mathematics , data mining , mathematical optimization , image (mathematics) , geography , geodesy
In this article, multiview spectral clustering via complementary information (MSCC) is proposed, in which both the consensus information and the complementary information are explored for multiview clustering. In contrast to most multiview spectral clustering methods, the proposed MSCC considers the differences among multiple views and constructs a similarity matrix for clustering. Furthermore, a convex relaxation is employed and an algorithm that is based on the augmented Lagrange multiplier is proposed for optimizing the objective function of MSCC. In extensive experiments on five real‐world benchmark datasets, our proposed method outperforms two baselines and has significantly improved to several state‐of‐the‐art multiview clustering methods.