Fractional Generalized Multiview Discriminative Projections and Extension for Multiview Learning

In multi-view learning, 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 multi-view linear discriminant analysis (GMLDA) usually fails to achieve good learning performance for many classification tasks. In this paper, we propose a new multi-view feature extraction method via fractional spectral modeling, namely, fractional generalized multi-view discriminant analysis (FGMDA), which is able to subsume GMLDA as a special case. In addition, an uncorrelated FGMDA (UFGMDA) is further proposed, where the extracted features are mutually uncorrelated in each view and thus have minimum redundancy. Extensive experimental results on visual recognition demonstrate the superiority of our proposed methods over the state-of-the-art methods in terms of classification accuracy.