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Fractional-Order Embedding Supervised Canonical Correlations Analysis with Applications to Feature Extraction and Recognition

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Abstract

Due to the noise disturbance and limited number of training samples, within-set and between-set sample covariance matrices in canonical correlations analysis (CCA) based methods usually deviate from the true ones. In this paper, we re-estimate the covariance matrices by embedding fractional order and incorporate the class label information. First, we illustrate the effectiveness of the fractional-order embedding model through theory analysis and experiments. Then, we quote fractional-order within-set and between-set scatter matrices, which can significantly reduce the deviation of sample covariance matrices. Finally, we incorporate the supervised information, novel generalized CCA and discriminative CCA are presented for multi-view dimensionality reduction and recognition, called fractional-order embedding generalized canonical correlations analysis and fractional-order embedding discriminative canonical correlations analysis. Extensive experimental results on various handwritten numeral, face and object recognition problems show that the proposed methods are very effective and obviously outperform the existing methods in terms of classification accuracy.

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Acknowledgments

This work is supported in part by Graduate Research and Innovation Foundation of Jiangsu Province, China, under Grant KYLX15_0379, and in part by the National Natural Science Foundation of China under Grants 61273251, 61401209, and 61402203.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing, 210094, People’s Republic of China

    Hong-Kun Ji, Quan-Sen Sun & Ze-Xuan Ji

  2. Department of Computer Science and Technology, Yangzhou University, Yangzhou, 225000, People’s Republic of China

    Yun-Hao Yuan

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  1. Hong-Kun Ji
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  2. Quan-Sen Sun
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  3. Yun-Hao Yuan
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  4. Ze-Xuan Ji
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Correspondence to Quan-Sen Sun.

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Ji, HK., Sun, QS., Yuan, YH. et al. Fractional-Order Embedding Supervised Canonical Correlations Analysis with Applications to Feature Extraction and Recognition. Neural Process Lett 45, 279–297 (2017). https://doi.org/10.1007/s11063-016-9524-z

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  • DOI: https://doi.org/10.1007/s11063-016-9524-z

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