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

In this paper, an efficient feature extraction method, named local structure preserving
discriminant analysis (LSPDA), is presented. LSPDA constructs the local scatter and the …

Dimensionality reduction is often required as a preliminary stage in many data analysis
applications. In this paper, we propose a novel supervised dimensionality reduction method …

In this paper an efficient feature extraction method named as locally linear discriminant
embedding (LLDE) is proposed for face recognition. It is well known that a point can be …

We propose in this paper two improved manifold learning methods called diagonal
discriminant locality preserving projections (Dia-DLPP) and weighted two-dimensional …

A large family of algorithms-supervised or unsupervised; stemming from statistics or
geometry theory-has been designed to provide different solutions to the problem of …

This paper proposes a novel locality preserving projections (LPP) algorithm for image
recognition, namely, the direct locality preserving projections (DLPP), which directly …

In this paper, we present an effective technique on discriminative feature extraction for face
recognition. The proposed technique incorporates Graph Embedding and the Fisher's …

In the last decades, a large family of algorithms-supervised or unsupervised; stemming from
statistic or geometry theory-have been proposed to provide different solutions to the problem …

Recently, L 1-norm-based robust discriminant feature extraction technique has been
attracted much attention in dimensionality reduction and pattern recognition. However, it …