Fisher linear discriminant analysis (FDA) and its kernel extension--kernel discriminant analysis (KDA)--are well known methods that consider dimensionality reduction and classific...
Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jor...
For quantitative analysis of histopathological images,
such as the lymphoma grading systems, quantification of
features is usually carried out on single cells before categorizing...
Hui Kong, Metin Gurcan, and Kamel Belkacem-Boussai...
A convenient way of dealing with image sets is to represent them as points on Grassmannian manifolds. While several recent studies explored the applicability of discriminant analy...
We develop a supervised dimensionality reduction method, called Lorentzian Discriminant Projection (LDP), for feature extraction and classification. Our method represents the str...
Abstract— One goal of statistical shape analysis is the discrimination between two populations of objects. In this paper, we present results of discriminant analysis on multi-obj...
Kevin Gorczowski, Martin Styner, Ja-Yeon Jeong, J....
: Laplacian Linear Discriminant Analysis (LapLDA) and Semi-supervised Discriminant Analysis (SDA) are two recently proposed LDA methods. They are developed independently with the a...
Feature extraction is among the most important problems in face recognition systems. In this paper, we propose an enhanced kernel discriminant analysis (KDA) algorithm called kern...
We present the theory for heteroscedastic discriminant analysis (HDA), a model-based generalization of linear discriminant analysis (LDA) derived in the maximum-likelihood framewo...
This paper introduces a novel nonlinear extension of Fisher's classical linear discriminant analysis (FDA) known as high-order Fisher's discriminant analysis (HOFDA). Th...