Document Detail


Information discriminant analysis: feature extraction with an information-theoretic objective.
MedLine Citation:
PMID:  17568143     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
Using elementary information-theoretic tools, we develop a novel technique for linear transformation from the space of observations into a low-dimensional (feature) subspace for the purpose of classification. The technique is based on a numerical optimization of an information-theoretic objective function, which can be computed analytically. The advantages of the proposed method over several other techniques are discussed and the conditions under which the method reduces to linear discriminant analysis are given. We show that the novel objective function enjoys many of the properties of the mutual information and the Bayes error and we give sufficient conditions for the method to be Bayes-optimal. Since the objective function is maximized numerically, we show how the calculations can be accelerated to yield feasible solutions. The performance of the method compares favorably to other linear discriminant-based feature extraction methods on a number of simulated and real-world data sets.
Authors:
Zoran Nenadic
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on pattern analysis and machine intelligence     Volume:  29     ISSN:  0162-8828     ISO Abbreviation:  IEEE Trans Pattern Anal Mach Intell     Publication Date:  2007 Aug 
Date Detail:
Created Date:  2007-06-14     Completed Date:  2007-08-30     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9885960     Medline TA:  IEEE Trans Pattern Anal Mach Intell     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1394-407     Citation Subset:  -    
Affiliation:
Department of Biomedical Engineering, University of California, Irvine, 3120 Natural Sciences II, Irvine, CA 92697-2715, USA. znenadic@uci.edu
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