CCA can be seen as a multiview extension of PCA, in which information from two sources is used for learning by finding a subspace in which the two views are most correlated. However PCA, and by extension CCA, does not use label information. Fisher Discriminant Analysis uses label information to find informative projections, which can be more informative in supervised learning settings. We deriv...

As a representative of the linear discriminant analysis, the Fisher method is most widely used in practice and it is very effective in twoclass classification. However, when it is expanded to a multi-class classification problem, the precision of its discrimination may become worse. A main reason is an occurrence of overlapped distributions on the discriminant space built by Fisher criterion. I...

This paper proposes a new method of feature extraction and recognition, namely, the fuzzy inverse Fisher discriminant analysis (FIFDA) based on the inverse Fisher discriminant criterion and fuzzy set theory. In the proposed method, a membership degree matrix is calculated using FKNN, then the membership degree is incorporated into the definition of the between-class scatter matrix and withinExp...

www.PosterPresentations.com • This paper presents a sparse representation based complete kernel marginal Fisher analysis (SCMFA) framework for categorizing fine art images. • First, we introduce several Fisher vector based features for feature extraction so as to extract and encode important discriminatory information of the painting image. • Second, we propose a complete marginal Fisher analys...

Local Fisher Discriminant Analysis (LFDA) is a feature extraction method which combines the ideas of Fisher discriminant analysis (FDA) and locality preserving projection (LPP). It works well for multimodal problems. But LFDA suffers from the under-sampled problem of the linear discriminant analysis (LDA). To deal with this problem, we propose a regularized orthogonal local Fisher discriminant ...

Sparsity-inducing multiple kernel Fisher discriminant analysis (MK-FDA) has been studied in the literature. Building on recent advances in non-sparse multiple kernel learning (MKL), we propose a non-sparse version of MK-FDA, which imposes a general lp norm regularisation on the kernel weights. We formulate the associated optimisation problem as a semi-infinite program (SIP), and adapt an iterat...