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A Novel Generalized Fuzzy Canonical Correlation Analysis Framework for Feature Fusion and Recognition

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Abstract

In this paper, a novel CCA-based dimensionality reduction method called generalized fuzzy canonical correlation analysis (GFCCA) is proposed. GFCCA combines the generalized canonical correlation analysis and fuzzy set theory. GFCCA redefines the fuzzy between-class and within-class scatter matrices that relate directly to the samples distribution information. For nonlinear separated problems, we extend the kernel extension of GFCCA with positive definite kernels and indefinite kernels. Experiments on real-world data sets are performed to test and evaluate the effectiveness of the proposed algorithms.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (61273251, 61673220).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Jing Yang & Quan-Sen Sun

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  1. Jing Yang
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  2. Quan-Sen Sun
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Correspondence to Quan-Sen Sun.

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Yang, J., Sun, QS. A Novel Generalized Fuzzy Canonical Correlation Analysis Framework for Feature Fusion and Recognition. Neural Process Lett 46, 521–536 (2017). https://doi.org/10.1007/s11063-017-9600-z

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