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| Presentation | 2015-01-29 16:05 Robustness of Tensor SOM for Missing Data Yasuhiro Wakita, Toru Iwasaki, Tetsuo Furukawa (Kyutech)  NC2014-61 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Tensor SOM is an extension of the self-organizing map (SOM), which enables us to visualize simultaneous visualization of multiple modes of relational data. One of the typical applications is user-item rating datasets of online shopping, which usually contain a lot of missing data. The aim of this work is to evaluate the robustness of the Tensor SOM to the data missing, as well as improving the algorithm. In this work, We examined two algorithms derived from different approaches. In the first approach, non-existent data are regarded as missing, which are need to be made up for during the learning. In contrast, the second approach regards that non-existent data are merely unobserved, just like the ordinary test dataset, and they are not needed to be estimated during the learning phase. The result showed that the algorithm derived from the second approach is superior to the first one. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | self-organizing map / Tensor SOM / relational data / missing value estimation / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 114, no. 437, NC2014-61, pp. 21-26, Jan. 2015. | |
| Paper # | NC2014-61 | |
| Date of Issue | 2015-01-22 (NC) | |
| 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 | NC2014-61 |
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| Conference Information | |
| Committee | NC |
| Conference Date | 2015-01-29 - 2015-01-30 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyushu Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Implementation of Neuro Computing,Analysis and Modeling of Human Science, etc |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2015-01-NC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Robustness of Tensor SOM for Missing Data |
| Sub Title (in English) | |
| Keyword(1) | self-organizing map |
| Keyword(2) | Tensor SOM |
| Keyword(3) | relational data |
| Keyword(4) | missing value estimation |
| Keyword(5) | |
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| Keyword(8) | |
| 1st Author's Name | Yasuhiro Wakita |
| 1st Author's Affiliation | Kyushu Institute of Technology (Kyutech) |
| 2nd Author's Name | Toru Iwasaki |
| 2nd Author's Affiliation | Kyushu Institute of Technology (Kyutech) |
| 3rd Author's Name | Tetsuo Furukawa |
| 3rd Author's Affiliation | Kyushu Institute of Technology (Kyutech) |
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| Speaker | Author-1 |
| Date Time | 2015-01-29 16:05:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2014-61 |
| Volume (vol) | vol.114 |
| Number (no) | no.437 |
| Page | pp.21-26 |
| #Pages | 6 |
| Date of Issue | 2015-01-22 (NC) |
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