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Paper Abstract and Keywords
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
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

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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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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