This paper develops an unsupervised discriminant projection (UDP) technique for
dimensionality reduction of high-dimensional data in small sample size cases. UDP can be …

This paper presents a new classifier called total margin-based adaptive fuzzy support vector
machines (TAF-SVM) that deals with several problems that may occur in support vector …

We consider the problem of locality preserving projections (LPP) in two-dimensional sense.
Recently, LPP was proposed for dimensionality reduction, which can detect the intrinsic …

This paper introduces a novel Gabor-based supervised locality preserving projection
(GSLPP) method for face recognition. Locality preserving projection (LPP) is a recently …

In this paper, a modified class of support vector machines (SVMs) inspired from the
optimization of Fisher's discriminant ratio is presented, the so-called minimum class variance …

When the feature dimension is larger than the number of samples the small sample-size
problem occurs. There is great concern about it within the face recognition community. We …

This paper first discusses some theoretical properties of 2D principal component analysis
(2DPCA) and then presents a horizontal and vertical 2DPCA-based discriminant analysis …

Maximum scatter difference (MSD) discriminant criterion was a recently presented binary
discriminant criterion for pattern classification that utilizes the generalized scatter difference …

Many pattern recognition applications involve the treatment of high-dimensional data and
the small sample size problem. Principal component analysis (PCA) is a common used …

In this paper, we propose a novel uncorrelated, weighted linear discriminant analysis
(UWLDA) method for feature extraction and recognition. The UWLDA first introduces a …