High-dimensional problems result in bad classification results because some combinations
of features have an adverse effect on classification; while class-imbalanced problems make …

In this paper, we propose a new discriminant locality preserving projections based on
maximum margin criterion (DLPP/MMC). DLPP/MMC seeks to maximize the difference …

Maximum margin criterion (MMC) based feature extraction is more efficient than linear
discriminant analysis (LDA) for calculating the discriminant vectors since it does not need to …

ABSTRACTTaranto (in the Apulia Region of southern Italy) has been included in a list of the
most polluted sites of national interest because of its large industrial area that is situated …

Recently, bidirectional principal component analysis (BDPCA) has been proven to be an
efficient tool for pattern recognition and image analysis. Encouraging experimental results …

Face recognition is a challenging problem due to variations in pose, illumination, and
expression. Techniques that can provide effective feature representation with enhanced …

On face recognition, most previous works on dimensionality reduction and classification
would first transform the input image into 1-D vector, which ignores the underlying data …

In this paper, we make a study on three linear discriminant analysis (LDA) based methods:
regularized discriminant analysis (RDA), discriminant common vectors (DCV) and maximal …

In the paper, fuzzy fisherface is extended to image matrix, namely, the fuzzy 2DLDA
(F2DLDA). In the proposed method, we calculate the membership degree matrix by fuzzy K …

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 …