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22nd Cumberland Conference on Combinatorics, Graph Theory and Computing
May 21-23, 2009
Western Kentucky University
Bowling Green, KY, USA

Organizers
Bela Csaba, Chair; Mustafa Atici; Robert Crawford; Claus Ernst; Dominic Lanphier; Attila Por

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Efficient and Incremental Linear Discriminant Analysis for High-Dimensional Data
by
Qi Li
Western Kentucky University
Coauthors: Jieping Ye

Applications in a wide range of areas involve high-dimensional data. Examples include face recognition, text classification, etc. Reducing the dimensionality is desirable to achieve efficient and accurate recognition/ classification results. Linear Discriminant Analysis (LDA) is a popular scheme for dimension reduction. We propose an efficient and incremental LDA method that has not been addressed in previous study.

Date received: April 30, 2009

Copyright © 2009 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # cayq-55.