Paper Abstract and Keywords |
Presentation |
2022-01-23 11:20
Towards 2-class classification via e(m)-PCA Jun Fujiki (Fukuoka Univ.), Shotaro Akaho (AIST) NC2021-43 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In many data analysis methods, observed data are treated as points in high-dimensional Euclidean space, called feature space by using linear and/or non-linear mapping from observed data into points in the feature space, and data are analized based on Euclidean distance. To compare among individuals when each individual has a set of data, each set of data should be regarded as a probabilistic distribution rather than points in high-dimensional Euclidean space, to understand the difference among individuals. Therefore, in this paper, we consider a data analysis method that treats probabilistic distributions as data points in a certain manifold. In this framework, it is difficult to apply the usual data analysis methods based on the Euclidean disntance because asymmetric distances such as the Kullback-Leibler divergence is used as the (pseudo-)distance of the probabilistic distribution. So far, principal component analysis, clustering, and clustering with dimension reduction have been proposed as data analysis for probabilistic distribution. However, discriminant analysis as supervised learning for probabilistic distribution is not proposed yet. Then in this paper,
we propose a discriminant analysis method based on e(m)-PCA as a prototype method toward a discriminant analysis method based on Kullback-Leibler divergence. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
information geometry / Kullback-Leibler divergence / principal component analysis / discriminant analysis / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 338, NC2021-43, pp. 55-58, Jan. 2022. |
Paper # |
NC2021-43 |
Date of Issue |
2022-01-14 (NC) |
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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NC2021-43 |
Conference Information |
Committee |
NLP MICT MBE NC |
Conference Date |
2022-01-21 - 2022-01-23 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
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(See Japanese page) |
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Paper Information |
Registration To |
NC |
Conference Code |
2022-01-NLP-MICT-MBE-NC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Towards 2-class classification via e(m)-PCA |
Sub Title (in English) |
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Keyword(1) |
information geometry |
Keyword(2) |
Kullback-Leibler divergence |
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principal component analysis |
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discriminant analysis |
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1st Author's Name |
Jun Fujiki |
1st Author's Affiliation |
Fukuoka University (Fukuoka Univ.) |
2nd Author's Name |
Shotaro Akaho |
2nd Author's Affiliation |
National Institute of Advanced Industrial Science and Technology (AIST) |
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Speaker |
Author-1 |
Date Time |
2022-01-23 11:20:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
NC2021-43 |
Volume (vol) |
vol.121 |
Number (no) |
no.338 |
Page |
pp.55-58 |
#Pages |
4 |
Date of Issue |
2022-01-14 (NC) |
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