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Paper Abstract and Keywords
Presentation 2010-09-06 13:40
[Fellow Memorial Lecture] -
Takio Kurita (Hiroshima Univ.) PRMU2010-84 IBISML2010-56
Abstract (in Japanese) (See Japanese page) 
(in English) Linear Discriminant Analysis (LDA) is one of the well known methods to extract good features for classification. Otsu derived the optimal nonlinear discriminant analysis (NDA) by assuming the underlying probabilities. This optimal NDA is closely related to Bayesian decision theory. Also Otsu showed that LDA could be interpreted as a linear approximation of the optimal NDA through the linear approximation of the Bayesian a posterior probabilities. Based on this theory on NDA, we can define a family of nonlinear discriminant analysis by changing the estimation method of the Bayesian a posterior probabilities. As an example, Logistic Discriminant Analysis (LgDA) is presented in this paper. To estimate a posterior probabilities, LgDA utilizes multi-nominal logistic regression (MLR) which is a member of generalized linear models. By this generalization of linear model, the discriminant space constructed by LgDA is drastically improved than the standard LDA.
Recently kernel Discriminant Analysis (KDA) has been often used in many applications. But the kernel function is usually defined a priori. To find the best kernel function for discriminant analysis, the kernel function used in the optimal NDA is investigated. The kernel function is also defined by using the Bayesian a posterior probabilities. This means that we can define a family of discriminate kernel functions by by changing the estimation method of the Bayesian a posterior probabilities.
Keyword (in Japanese) (See Japanese page) 
(in English) nonlinear discriminant analysis / Bayes decition theory / logistic dicriminant analysis / discriminant kernel / / / /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 187, PRMU2010-84, pp. 209-214, Sept. 2010.
Paper # PRMU2010-84 
Date of Issue 2010-08-29 (PRMU, IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF PRMU2010-84 IBISML2010-56

Conference Information
Committee IBISML PRMU IPSJ-CVIM  
Conference Date 2010-09-05 - 2010-09-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Fukuoka Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine learning and optimization for computer vision and pattern recognition, etc. 
Paper Information
Registration To PRMU 
Conference Code 2010-09-IBISML-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) - 
Sub Title (in English)  
Keyword(1) nonlinear discriminant analysis  
Keyword(2) Bayes decition theory  
Keyword(3) logistic dicriminant analysis  
Keyword(4) discriminant kernel  
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1st Author's Name Takio Kurita  
1st Author's Affiliation Hiroshima University (Hiroshima Univ.)
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Speaker Author-1 
Date Time 2010-09-06 13:40:00 
Presentation Time 60 minutes 
Registration for PRMU 
Paper # PRMU2010-84, IBISML2010-56 
Volume (vol) vol.110 
Number (no) no.187(PRMU), no.188(IBISML) 
Page pp.209-214 
#Pages 6 
Date of Issue 2010-08-29 (PRMU, IBISML) 

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