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| Presentation | 2021-03-02 10:50 Kernel tensor decomposition based unsupervised feature extraction -- Applications to bioinformatics -- Y-h. Taguchi (Chuo Univ.)  IBISML2020-36 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | A lot of research has been done on the so-called textit{large p small n} problem, where the number of samples is small compared to the number of variables. In the so-called field of genome science, however, this ratio is extreme, with the number of genes (= number of variables = $p$) being tens of thousands while the number of subjects (= number of samples = $n$) is even a few, and $p/n sim 10^3$ is not uncommon. In such extreme cases, many of the methods proposed for the so-called textit{large p small n}problem are often ineffective. We have proposed ``Principal component analysis and tensor decomposition based unsupervised feature extraction'' to deal with this problem. In the past decade, we have applied this method to many researches in the field of bioinformatics. However, this method is purely within the scope of linear algebra, and since it is unsupervised learning, there is no tuning parameter, and if the method does not work, there is no choice but to give up on the analysis itself. In order to solve this problem, we have developed a kernel version of the method. In this paper, we report on how we succeeded in kernelizing the method to solve this problem, which enables the method to take nonlinear relationships into account and greatly expands its application range. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Tensor decomposition / Kernel trick / Feature selection / Unsupervised learning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 395, IBISML2020-36, pp. 16-23, March 2021. | |
| Paper # | IBISML2020-36 | |
| Date of Issue | 2021-02-23 (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) | |
| Download PDF | IBISML2020-36 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2021-03-02 - 2021-03-04 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Organized and general sessions on machine learning |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2021-03-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Kernel tensor decomposition based unsupervised feature extraction |
| Sub Title (in English) | Applications to bioinformatics |
| Keyword(1) | Tensor decomposition |
| Keyword(2) | Kernel trick |
| Keyword(3) | Feature selection |
| Keyword(4) | Unsupervised learning |
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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 | 2021-03-02 10:50:00 |
| Presentation Time | 25 minutes |
| Registration for | IBISML |
| Paper # | IBISML2020-36 |
| Volume (vol) | vol.120 |
| Number (no) | no.395 |
| Page | pp.16-23 |
| #Pages | 8 |
| Date of Issue | 2021-02-23 (IBISML) |
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