期刊
NEURAL NETWORKS
卷 98, 期 -, 页码 178-191出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2017.11.013
关键词
Kernel CCA; Randomized Kaczmarz method; Reproducing kernel Hilbert space; Cross-language document retrieval; Content-based image retrieval
资金
- National Natural Science Foundation of China [11401112, 61462096]
- National Statistical Science Research Program [2016LZ47]
- Science and Technology Program of Guangzhou [201707010228]
- China Scholarship Council
Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods. (C) 2017 Elsevier Ltd. All rights reserved.
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