Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction
problems as a preprocessing step for machine learning and pattern classification …

Sparse subspace learning has drawn more and more attentions recently. However, most of
the sparse subspace learning methods are unsupervised and unsuitable for classification …

This paper develops a new dimensionality reduction method, named biomimetic
uncorrelated locality discriminant projection (BULDP), for face recognition. It is based on …

In this paper, a modification of the Sugeno integral with interval type-2 fuzzy logic is
proposed. The modification includes changing the original equations of the Sugeno …

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 …

High-dimensional small sample size data, which may lead to singularity in computation, are
becoming increasingly common in the field of pattern recognition. Moreover, it is still an …

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

Conventional discriminant locality preserving projection (DLPP) is a dimensionality
reduction technique based on manifold learning, which has demonstrated good …

Dimensionality reduction techniques aim to transform the high-dimensional data into a
meaningful reduced representation and have been consistently playing a fundamental role …

Locality preserving projections (LPP) is a popular unsupervised dimensionality reduction
method based on manifold learning. As a supervised version of the LPP method …