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KPCA method based on within-class auxiliary training samples and its application to pattern classification

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Abstract

Principal component analysis (PCA) and kernel principal component analysis (KPCA) are classical feature extraction methods. However, PCA and KPCA are unsupervised learning methods which always maximize the overall variance and ignore the information of within-class and between-class. In this paper, we propose a simple yet effective strategy to improve the performance of PCA and then this strategy is generalized to KPCA. The proposed methods utilize within-class auxiliary training samples, which are constructed through linear interpolation method. These within-class auxiliary training samples are used to train and get the principal components. In contrast with conventional PCA and KPCA, our proposed methods have more discriminant information. Several experiments are respectively conducted on XM2VTS face database, United States Postal Service (USPS) handwritten digits database and three UCI repository of machine learning databases, experimental results illustrate the effectiveness of the proposed method.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under grant No. 61373055 and No. 61103128.

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Authors and Affiliations

  1. School of IoT Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Sugen Chen, Xiaojun Wu & Hefeng Yin

  2. School of Mathematics and Computational Science, Anqing Normal University, Anqing, 246133, People’s Republic of China

    Sugen Chen

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  1. Sugen Chen
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  2. Xiaojun Wu
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  3. Hefeng Yin
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Correspondence to Xiaojun Wu.

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Chen, S., Wu, X. & Yin, H. KPCA method based on within-class auxiliary training samples and its application to pattern classification. Pattern Anal Applic 20, 749–767 (2017). https://doi.org/10.1007/s10044-016-0531-5

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