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| Paper Abstract and Keywords | ||
| Presentation | 2019-04-19 10:55 Blind Source Separation in Nonlinear Mixture: Separation and a Multi-Subspace Representation Lu Wang, Tomoaki Ohtsuki (Keio Univ.)  RCS2019-16 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The process deals with blind source separation in the nonlinear domain is to estimate the original signals or mixture functions from the degraded signals, without any prior information about the mixing functions. The fundamental problem is to recover the original sources by estimating an approximation function under such assumptions so as to estimate the inverse of mixing functions. However, in practice, the approximation function is derived from some estimation algorithm with a finite sample size that even larger estimation error appears with improper model construction. In this paper, we work on the convergence and asymptotic analysis of the separation approach, where the nonlinearity of the mixture function is extracted by the flexible approximation and the nonlinear problem is solved linearly in the feature space. The analysis stems from the performance of a mismatched estimator that accesses the finite sample size. By providing a closed-form expression of the mean squared error (MSE), we can present a novel algebraic formalization as well as derive an upper bound on the estimation error. The simulation results show that if the nonlinearity of mixing functions can be extracted by the flexible approximation, the consistency of numerical MSE and analytical MSE can be achieved as the sample size tends to be infinity. This implies that the algorithm is feasible to separate the distortion of the nonlinear mixture. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Nonlinear blind source separation / multi-subspace representation / nonlinear mixture / kernel mapping / time-frequency representation / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 8, RCS2019-16, pp. 73-78, April 2019. | |
| Paper # | RCS2019-16 | |
| Date of Issue | 2019-04-11 (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 | RCS2019-16 |
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| Conference Information | |
| Committee | RCS |
| Conference Date | 2019-04-18 - 2019-04-19 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Noboribetsu Grand Hotel |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Railroad Communications, Inter-Vehicle Communications, Road to Vehicle Communications, Radio Access Technologies, Wireless Communications, etc. |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2019-04-RCS |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Blind Source Separation in Nonlinear Mixture: Separation and a Multi-Subspace Representation |
| Sub Title (in English) | |
| Keyword(1) | Nonlinear blind source separation |
| Keyword(2) | multi-subspace representation |
| Keyword(3) | nonlinear mixture |
| Keyword(4) | kernel mapping |
| Keyword(5) | time-frequency representation |
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| Keyword(8) | |
| 1st Author's Name | Lu Wang |
| 1st Author's Affiliation | Keio Univeristy (Keio Univ.) |
| 2nd Author's Name | Tomoaki Ohtsuki |
| 2nd Author's Affiliation | Keio Univeristy (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2019-04-19 10:55:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCS2019-16 |
| Volume (vol) | vol.119 |
| Number (no) | no.8 |
| Page | pp.73-78 |
| #Pages | 6 |
| Date of Issue | 2019-04-11 (RCS) |
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