Discriminant analysis is effective in extracting discriminative features and reducing
dimensionality. In this paper, we propose an optimal subset-division based discrimination …

In the field of image processing and recognition, discrete cosine transform (DCT) and linear
discrimination are two widely used techniques. Based on them, we present a new face and …

Linear discriminant analysis (LDA)-based methods have been very successful in face and
palmprint recognition. Recently, a class of post-processing approaches has been proposed …

Abstract In existing Linear Discriminant Analysis (LDA) models, the class population mean is
always estimated by the class sample average. In small sample size problems, such as face …

In this paper, the method of kernel direct discriminant analysis is analyzed from a new
viewpoint and its theoretical foundation is revealed. Based on this result, an efficient and …

A new subspace learning algorithm called locality preserving discriminant projections
(LPDP) is proposed by adding the criterion of maximum margin criterion (MMC) into the …

This paper proposes a new image feature extraction method for face recognition, called dual-
kernel based two dimensional linear discriminant analysis (D-K2DLDA), by integrating …

In this paper, we propose a new kernel discriminant analysis called kernel relevance
weighted discriminant analysis (KRWDA) which has several interesting characteristics. First …

Small sample size and high computational complexity are two major problems encountered
when traditional kernel discriminant analysis methods are applied to high-dimensional …

As is known, kernel methods are developed to handle nonlinear classification. However, we
have found that based on vector representation of face images, it is not easy to improve the …