Many high dimensional classification techniques have been proposed in the literature based on sparse linear discriminant analysis (LDA). To efficiently use them, sparsity of linear classifiers is a prerequisite. However, this might not be readily available in many applications, and rotations of data are required to create the needed sparsity. In this paper, we propose a family of rotations to c...

Mika, Rätsch, Weston, Schölkopf and Müller [Mika, S., Rätsch, G., Weston, J., Schölkopf, B., & Müller, K.-R. (1999). Fisher discriminant analysis with kernels. In Neural networks for signal processing: Vol. IX (pp. 41-48). New York: IEEE Press] introduce a non-linear formulation of Fisher's linear discriminant, based on the now familiar "kernel trick", demonstrating state-of-the-art performance...

1. student research committee, urmia university of medical sciences, urmia, iran 2. dept. of medical physics, faculty of medicine, urmia university of medical sciences, urmia, iran 3. dept. of radiology, faculty of medicine, imam khomeini hospital, urmia university of medical sciences, urmia, iran                             corresponding author: akbar gharbali, phd; assistant professor of medi...

There are two fundamental problems with the Fisher linear discriminant analysis for face recognition. One is the singularity problem of the within-class scatter matrix due to small training sample size. The other is that it cannot efficiently describe complex nonlinear variations of face images because of its linear property. In this letter, a kernel scatter-difference-based discriminant analys...

In this paper, a new technique called Two Dimensional Gabor Fisher Discriminant (2DGFD) is derived and implemented for image representation and recognition. In our approach, the Gabor wavelets are used to extract facial features. The Principal Component Analysis (PCA) is applied directly on the Gabor transformed matrices to remove redundant information from the image rows and a new direct two d...

Graph-based Fisher Analysis (GbFA) is proposed recently for dimensionality reduction, which has the powerful discriminant ability. However, GbFA is based on the matrix-to-vector way, which not only costs much but also loses spatial relations of pixels in images. Therefore, Tensor Graph-based Linear Discriminant Analysis (TGbLDA) is proposed in the paper. TGbLDA regards samples as data in tensor...