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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IBISML2018-6 |
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) |
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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 |
7 |
Date of Issue |
2018-06-06 (IBISML) |