Abstract
In image recognition, there are a large number of small sample size problems in which the number of training samples is less than the dimension of feature vectors. For such problems, generalized multiview linear discriminant analysis (GMLDA) usually fails to achieve good learning performance for many classification tasks. With the idea of fractional order embedding, this paper proposes a new multiview feature learning method via fractional spectral modeling, namely, fractional-order generalized multiview discriminant analysis (FGMDA), which is able to subsume GMLDA as a special case. Experimental results on visual recognition have demonstrated the effectiveness of the proposed method and shown that FGMDA outperforms GMLDA.
Supported by the National Natural Science Foundation of China under Grant Nos. 61402203, 61472344, 61611540347, and 61703362, Natural Science Fund of Jiangsu under Grant Nos. BK20161338 and BK20170513, and Yangzhou Science Fund under Grant Nos. YZ2017292 and YZ2016238. Moreover, it is also sponsored by the Excellent Young Backbone Teacher (Qing Lan) Fund and Scientific Innovation Research Fund of Yangzhou University under Grant No. 2017CXJ033.
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Yuan, YH. et al. (2018). Generalized Multiview Discriminative Projections with Spectral Reconstruction. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_40
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DOI: https://doi.org/10.1007/978-3-030-02698-1_40
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