Orthogonal Linear Discriminant Analysis Jieping Ye JIEPING.  Home/Search
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Abstract: Dimensionality reduction is an important pre-processing step in many applications. Linear discriminant analysis (LDA) is a classical statistical approach for supervised dimensionality reduction. It aims to maximize the ratio of the between-class distance to the within-class distance, thus maximizing the class discrimination. It has been used widely in many applications. However, the classical LDA formulation requires the nonsingularity of the scatter matrices involved. For undersampled... (Update)
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BibTeX entry: (Update)
@misc{ discriminant-journal,
author = "Orthogonal Linear Discriminant",
title = "Journal of Machine Learning Research 7 (2006) 1183--1204 Submitted 12/05;
Revised 3/06; Published 7/06 Computational and Theoretical Analysis of Null
Space and",
url = "citeseer.ist.psu.edu/757897.html" }
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