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

被引：0
作者：
Jing Yang
Quan-Sen Sun
机构：
[1] Nanjing University of Science and Technology,School of Computer Science and Engineering
来源：
Neural Processing Letters | 2017年 / 46卷
关键词：
Canonical correlation analysis; Generalized canonical correlation analysis; Fuzzy membership; Feature fusion; Dimensionality reduction;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：521 / 536
页数：15
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