Fuzzy fractional canonical correlation analysis

Min Ruan; Yun Li; Yun-Hao Yuan; Ji-Peng Qiang; Bin Li; Xiao-Bo Shen

doi:10.1117/12.2503066

9 August 2018 Fuzzy fractional canonical correlation analysis

Min Ruan, Yun Li, Yun-Hao Yuan, Ji-Peng Qiang, Bin Li, Xiao-Bo Shen

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108060A (2018) https://doi.org/10.1117/12.2503066
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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.

Citation Download Citation

Min Ruan, Yun Li, Yun-Hao Yuan, Ji-Peng Qiang, Bin Li, and Xiao-Bo Shen "Fuzzy fractional canonical correlation analysis", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060A (9 August 2018); https://doi.org/10.1117/12.2503066

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