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| Presentation | 2019-10-25 16:05 [Encouragement Talk] Nonlinear Feature Extraction by epsilon -Vanishing Polynomial Networks Learning and Underdetermined BSS Lu Wang, Tomoaki Ohtsuki (Keio Univ.)  RCS2019-202 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Similar to the deep architectures, a novel multi-layer architecture is used to extend the linear blind source separation (BSS) method to the nonlinear case in this report. The approach approximates the nonlinearities based on a polynomial network, where the layer of our network begins with the polynomial of degree $1$, up to build an output layer that can represent data with a small bias by a good approximate basis. Relying on several transformations of the input data, with higher-level representation from lower-level ones, the networks are to fulfill a mapping implicitly to the high-dimensional space. Once the polynomial networks are built, the coefficient matrix can be estimated by solving an $l_1$-regularization on the coding coefficient vector. The experiment shows that the proposed approach exhibits a higher separation accuracy than the comparison algorithms. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Nonlinear blind source separation / performance analysis / vanishing component analysis / temporal structure / independent component analysis / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 244, RCS2019-202, pp. 133-138, Oct. 2019. | |
| Paper # | RCS2019-202 | |
| Date of Issue | 2019-10-17 (RCS) | |
| 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 | RCS2019-202 |
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| Conference Information | |
| Committee | RCS |
| Conference Date | 2019-10-24 - 2019-10-25 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Yokosuka Research Park |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Wireless Communication Schemes, Wireless Communication Systems, Wireless Standards, Future Wireless Systems, etc. |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2019-10-RCS |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Nonlinear Feature Extraction by epsilon -Vanishing Polynomial Networks Learning and Underdetermined BSS |
| Sub Title (in English) | |
| Keyword(1) | Nonlinear blind source separation |
| Keyword(2) | performance analysis |
| Keyword(3) | vanishing component analysis |
| Keyword(4) | temporal structure |
| Keyword(5) | independent component analysis |
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| Keyword(8) | |
| 1st Author's Name | Lu Wang |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Tomoaki Ohtsuki |
| 2nd Author's Affiliation | Keio University (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2019-10-25 16:05:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCS2019-202 |
| Volume (vol) | vol.119 |
| Number (no) | no.244 |
| Page | pp.133-138 |
| #Pages | 6 |
| Date of Issue | 2019-10-17 (RCS) |
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