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Fuzzy fractional canonical correlation analysis
Author(s): Min Ruan; Yun Li; Yun-Hao Yuan; Ji-Peng Qiang; Bin Li; Xiao-Bo Shen
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Paper Abstract

Feature learning has been widely used for image recognition. However, limited training samples and much noise usually make it challenging in practical classification applications. Specifically, it makes sample covariance matrix usually deviate from true ones. To alleviate this bias, we utilize a fractional-order strategy to re-model sample spectra of covariance matrix. On the other hand, as the object classes’ boundary is not very clear in practice, it is necessary to incorporate fuzzy relationship into feature learning. In this paper, we propose a fuzzy fractional canonical correlation analysis (FFCCA), where sample spectra are reconstructed by fractional modeling and at the same time, fuzzy label information is considered. Experimental results on visual recognition have shown that FFCCA can learn more discriminative low-dimensional features, in contrast with existing feature learning methods.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060A (9 August 2018); doi: 10.1117/12.2503066
Show Author Affiliations
Min Ruan, Yangzhou Univ. (China)
Yun Li, Yangzhou Univ. (China)
Yun-Hao Yuan, Yangzhou Univ. (China)
Ji-Peng Qiang, Yangzhou Univ. (China)
Bin Li, Yangzhou Univ. (China)
Xiao-Bo Shen, Nanjing Univ. of Science and Technology (Singapore)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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