Over the years, many discriminant analysis (DA) algorithms have been proposed for the
study of high-dimensional data in a large variety of problems. Each of these algorithms is …

In this paper, two supervised methods for enhancing the classification accuracy of the
Nonnegative Matrix Factorization (NMF) algorithm are presented. The idea is to extend the …

This paper presents a novel pattern recognition framework by capitalizing on dimensionality
increasing techniques. In particular, the framework integrates Gabor image representation, a …

Recently, the wide deployment of practical face recognition systems gives rise to the
emergence of the inter-modality face recognition problem. In this problem, the face images …

Dimensionality reduction is an important pre-processing step in many applications. Linear
discriminant analysis (LDA) is a classical statistical approach for supervised dimensionality …

In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant
analysis in a high dimensional feature space defined implicitly by a kernel. The performance …

In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly
nonlinear mapping is proposed for human face recognition. In our approach, the Gabor …

Locality preserving projections (LPP) is a new subspace feature extraction method which
seeks to preserve the local structure and intrinsic geometry of the data space. As the LPP …

Several pattern classifiers give high classification accuracy but their storage requirements
and processing time are severely expensive. On the other hand, some classifiers require …

Dimensionality reduction is an important pre-processing step for many applications. Linear
Discriminant Analysis (LDA) is one of the well known methods for supervised dimensionality …