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Paper Abstract and Keywords
Presentation 2018-06-13 13:25
A supervised dimensionality reduction method using linear combinations of multiple eigenvectors
Akira Imakura, Momo Matsuda, Tetsuya Sakurai (Univ. Tsukuba) IBISML2018-6
Abstract (in Japanese) (See Japanese page) 
(in English) Dimensionality reduction methods that reduce the dimension of original data to a low-dimensional subspace such as LPP and LFDA are widely used for clustering and classifications. These dimensionality reduction methods are formulated by minimization or maximization of a matrix trace and solved as few eigenvectors of the corresponding generalized eigenvalue problem. In this paper, based on the concept of the dimensionality reduction methods, we propose a novel supervised dimensionality reduction method that constructs a low-dimensional subspace with linear combinations of multiple eigenvectors. The proposed method needs to compute multiple eigenvectors; however, one can solve them efficiently by complex moment-based parallel eigensolvers.
Keyword (in Japanese) (See Japanese page) 
(in English) dimensionality reduction methods / supervised learning / linear combination of eigenvectors / complex moment-based eigensolvers / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 81, IBISML2018-6, pp. 39-45, June 2018.
Paper # IBISML2018-6 
Date of Issue 2018-06-06 (IBISML) 
ISSN 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)
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Conference Information
Committee NC IBISML IPSJ-BIO IPSJ-MPS  
Conference Date 2018-06-13 - 2018-06-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2018-06-NC-IBISML-BIO-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A supervised dimensionality reduction method using linear combinations of multiple eigenvectors 
Sub Title (in English)  
Keyword(1) dimensionality reduction methods  
Keyword(2) supervised learning  
Keyword(3) linear combination of eigenvectors  
Keyword(4) complex moment-based eigensolvers  
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1st Author's Name Akira Imakura  
1st Author's Affiliation University of Tsukuba (Univ. Tsukuba)
2nd Author's Name Momo Matsuda  
2nd Author's Affiliation University of Tsukuba (Univ. Tsukuba)
3rd Author's Name Tetsuya Sakurai  
3rd Author's Affiliation University of Tsukuba (Univ. Tsukuba)
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Speaker Author-1 
Date Time 2018-06-13 13:25:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2018-6 
Volume (vol) vol.118 
Number (no) no.81 
Page pp.39-45 
#Pages
Date of Issue 2018-06-06 (IBISML) 


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