Abstract
Different with typical supervised canonical correlation methods where intraclass and interclass information of samples are exploited at the same time for classification tasks, in this paper, we follow the principle of Occam’s razor and thus propose a new supervised multiview dimensionality reduction method for image recognition, called semi-discriminative multiview canonical correlations (SemiDMCCs), which takes partial class information into account but generates discriminative low-dimensional projections. Experimental results on benchmark databases show the more effectiveness of the proposed method, in contrast to existing feature reduction methods.
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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., Ji, HK., Ren, CG., Shen, XB., Sun, QS. (2016). Semi-discriminative Multiview Canonical Correlation Analysis for Recognition. 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_28
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DOI: https://doi.org/10.1007/978-3-319-46257-8_28
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