Paper Abstract and Keywords |
Presentation |
2021-07-09 14:30
Construction of Dimension Reduction Matrix for Signal Recovery of Multivariate Gaussian Vectors Kento Yokoyama, Tadashi Wadayama, Satoshi Takabe (NIT) IT2021-26 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In compressed sensing, we discuss the problem of estimating the sparse original signal $¥bm{x} ¥in ¥mathbb{R}^n$ from the linear observational signal $¥bm{y} = ¥bm{Ax}+ ¥bm{n} ¥in ¥mathbb{R}^m$.
This signal reconstruction problem is an inferior decision problem in which the observed signal length $m$ is shorter than the original signal length $n$.
As a signal reconstruction problem based on the same linear observation, in this paper, we consider the case where original signal $¥bm{x}$ is a signal that follows the multivariate Gaussian distribution $¥mathcal{N}(¥bm{0}, ¥bm{K})$.
The MMSE estimator function is used for signal reconstruction.
In this paper, we consider the optimal design problem of the observation matrix $¥bm A$.
That is, on the premise that we can design the observation matrix $¥bm {A}$, we propose a method of constructing $¥bm{A}$ that minimizes the estimation error.
Based on the singular value representation of the MMSE estimation error (MSE), the proposed observation matrix can be obtained by optimizing the singular value of the observation matrix by the method of Lagrange multiplier.
Experiments have shown that the proposed observation matrix gives a smaller estimation error than the existing method. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
compressed sensing / MMSE estimation / dimension reduction / multivariate Gaussian distribution / singular value representation / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 96, IT2021-26, pp. 63-68, July 2021. |
Paper # |
IT2021-26 |
Date of Issue |
2021-07-01 (IT) |
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) |
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IT2021-26 |
Conference Information |
Committee |
IT |
Conference Date |
2021-07-08 - 2021-07-09 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Freshman session, General |
Paper Information |
Registration To |
IT |
Conference Code |
2021-07-IT |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Construction of Dimension Reduction Matrix for Signal Recovery of Multivariate Gaussian Vectors |
Sub Title (in English) |
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Keyword(1) |
compressed sensing |
Keyword(2) |
MMSE estimation |
Keyword(3) |
dimension reduction |
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multivariate Gaussian distribution |
Keyword(5) |
singular value representation |
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1st Author's Name |
Kento Yokoyama |
1st Author's Affiliation |
Nagoya Institute of Technology (NIT) |
2nd Author's Name |
Tadashi Wadayama |
2nd Author's Affiliation |
Nagoya Institute of Technology (NIT) |
3rd Author's Name |
Satoshi Takabe |
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Nagoya Institute of Technology (NIT) |
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Speaker |
Author-1 |
Date Time |
2021-07-09 14:30:00 |
Presentation Time |
25 minutes |
Registration for |
IT |
Paper # |
IT2021-26 |
Volume (vol) |
vol.121 |
Number (no) |
no.96 |
Page |
pp.63-68 |
#Pages |
6 |
Date of Issue |
2021-07-01 (IT) |
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