This paper proposes a novel method, called two-dimensional local graph embedding
discriminant analysis (2DLGEDA), for image feature extraction, which can directly extract the …

In this paper, we propose a novel method for image feature extraction, namely the two-
dimensional local graph embedding, which is based on maximum margin criterion and thus …

In this paper, we propose a new supervised linear dimensionality reduction method called
discriminant projection embedding (DPE). DPE can preserve within-class neighboring …

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 …

This paper introduces a novel dimensionality reduction algorithm, called collaborative
representation based local discriminant projection (CRLDP), for feature extraction. CRLDP …

In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an
image feature extraction and pattern recognition based on graph embedded learning and …

Two-dimensional local graph embedding discriminant analysis (2DLGEDA) and two-
dimensional discriminant locality preserving projections (2DDLPP) were recently proposed …

In this paper we propose a novel method combining graph embedding and difference
criterion techniques for image feature extraction, namely two-dimensional maximum …

Recently, two-dimensional locality preserving projections (2DLPP) was proposed to extract
features directly from image matrices based on locality preserving criterion. Though 2DLPP …

Linear discriminant analysis (LDA) is a powerful dimensionality reduction technique, which
has been widely used in many applications. Although, LDA is well-known for its discriminant …