Abstract
Multi-view discriminant analysis (MvDA) is a powerful method for dimensionality reduction. However, intraclass and interclass sample scatter matrices in MvDA will deviate from true ones due to noise or limited training samples. To reduce the negative effect of the bias, in this paper we propose a novel method for learning multi-view low-dimensional representations, called fractional-order multi-view discriminant analysis (FMDA), which is based on fractional-order dispersion matrices built by sample spectrum reconstruction. Moreover, MvDA can be viewed as a special case of FMDA. A series of experiments show FMDA is effective and overall outperforms the state-of-the-art method MvDA.
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Notes
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Matlab code available at http://vipl.ict.ac.cn/resources/codes.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant Nos. 61402203, 61273251, 61170120, Natural Science Foundation of Jiangsu Province of China under Grant No. BK20161338, and the Fundamental Research Funds for the Central Universities under Grant No. JUSRP11458. Moreover, it is also supported by the Program for New Century Excellent Talents in University under Grant No. NCET-12-0881.
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Yuan, YH., Li, Y., Shen, XB., Ren, CG., Li, CF. (2016). Fractional-Order Multiview Discriminant Analysis. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_42
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