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Composite nonlinear multiset canonical correlation analysis for multiview feature learning and recognition
Author(s) -
Yuan YunHao,
Shen Xiaobo,
Li Yun,
Li Bin,
Gou Jianping,
Qiang Jipeng,
Zhang Xinfeng,
Sun QuanSen
Publication year - 2019
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.5476
Subject(s) - multiset , canonical correlation , mathematics , robustness (evolution) , hilbert space , nonlinear system , pattern recognition (psychology) , redundancy (engineering) , reproducing kernel hilbert space , kernel (algebra) , algorithm , artificial intelligence , feature vector , correlation , computer science , discrete mathematics , pure mathematics , geometry , biochemistry , chemistry , physics , quantum mechanics , gene , operating system
Summary In this paper, we propose a composite nonlinear multiset canonical correlation projections (CNMCPs) framework where orthogonal constraints are imposed in each set. This makes CNMCP capable of learning uncorrelated low‐dimensional features with minimum redundancy in Hilbert space. With the CNMCP framework, we further present a particular algorithm called multikernel multiset canonical correlations or mKMCC, which introduces different weights into multiple nonlinear functions in all views. An alternating iterative optimization is designed for computational solution. Numerous experimental results on practical datasets have demonstrated the effectiveness and robustness of mKMCC, in contrast with existing kernel correlation learning approaches.