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| Presentation | 2016-11-16 15:00 [Poster Presentation] Principal Component Analysis based unsupervised Feature Extraction applied to Bioinformatics Y-h. Taguchi (Chuo Univ.)  IBISML2016-47 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, numerous researches were performed for the machine/statisitical learning. Among those, deep learning is especially outstanding method. As an basis of its high performance, unsupervised feature extraction by autoencoder was considered to be ciritical. However, since autosncoder require huge number of un-labeled data sets, it is not always applicable. The author proposed an alternative method, principal component analysis (PCA) based unsupervised feature extraction (FE), which can work with small number of un-labeled data so as to be applicable to bioinformatics problem where small number ($n$) of samples are often available although there are typically many number ($p$) of state variables ($n ll p$). In this report we discuss about the recent progress of PCA based unsupervised FE as well as the proposal and application of tensor decomposition based unsupervised feature extraction. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | unsupervised learning / principal component analysis / tensor decomposition / feature extraction / bioinformatics / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 300, IBISML2016-47, pp. 17-24, Nov. 2016. | |
| Paper # | IBISML2016-47 | |
| Date of Issue | 2016-11-09 (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 | IBISML2016-47 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2016-11-16 - 2016-11-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyoto Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Information-Based Induction Science Workshop (IBIS2016) |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2016-11-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Principal Component Analysis based unsupervised Feature Extraction applied to Bioinformatics |
| Sub Title (in English) | |
| Keyword(1) | unsupervised learning |
| Keyword(2) | principal component analysis |
| Keyword(3) | tensor decomposition |
| Keyword(4) | feature extraction |
| Keyword(5) | bioinformatics |
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| 1st Author's Name | Y-h. Taguchi |
| 1st Author's Affiliation | Chuo University (Chuo Univ.) |
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| Speaker | Author-1 |
| Date Time | 2016-11-16 15:00:00 |
| Presentation Time | 180 minutes |
| Registration for | IBISML |
| Paper # | IBISML2016-47 |
| Volume (vol) | vol.116 |
| Number (no) | no.300 |
| Page | pp.17-24 |
| #Pages | 8 |
| Date of Issue | 2016-11-09 (IBISML) |
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