This paper proposes a novel algorithm for image feature extraction, namely, the two-
dimensional locality preserving projections (2DLPP), which directly extracts the proper …

In this paper, a supervised feature extraction method, named orthogonal discriminant
projection (ODP), is presented. As an extension of spectral mapping method, the proposed …

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

Discriminant neighborhood embedding (DNE) is a typical graph-based dimensionality
reduction method, and has been successfully applied to face recognition. By constructing an …

Abstract Two-dimensional (2D) local discriminant analysis is one of the popular techniques
for image representation and recognition. Conventional 2D methods extract features of …

Low-dimensional feature representation with enhanced discriminatory power of paramount
importance to face recognition systems. Most of traditional linear discriminant analysis (LDA) …

Abstract Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the
class relationship between data points. A major disadvantage of LDA is that it fails to …

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach.
The algorithms of LDA usually perform well under the following two assumptions. The first …

In this paper we propose a new linear feature extraction approach called Weighted Linear
Embedding (WLE). WLE combines Fisher criterion with manifold learning criterion like local …

Two-dimensional locality-preserving projection (2DLPP) is an unsupervised method, so it
can't use the discrimination information of the sample in the sparse data; elastic net …