Paper Abstract and Keywords |
Presentation |
2014-11-21 11:50
Hyper-parameter estimation for compressive sensing with a Bernoulli-Gauss prior distribution Toshiyuki Watanabe, Jun-ichi Inoue (Hokkaido Univ.) NC2014-28 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Compressive sensing is a theory that estimates sparse
information signals which has few non-zero elements
from less observations. In terms of Bayesian estimation,
Laplasian distribution ($L_{1}$-regularization) is normally
chosen for the prior distribution. However, if a true
distribution is a Bernoulli normal distribution whose zero
or non-zero element is generated by a Bernoulli distribution
of a non-zero rate (sparse rate as a `hyper-parameter') and
the non-zero elements is normally distributed, the Bernoulli
normal distribution should be chosen for a candidate
of the prior distribution in the sense of Bayesian optimality.
In this paper, we evaluate a dependence of
the hyper-parameter on the mean-square error by
replica method and discuss the dynamics of hyper-parameter
estimation by means of EM algorithm to maximize
the marginal likelihood indirectly. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Compressive sensing / Statistical mechanics / Replica method / EM algorithm / Markov chain Monte Carlo method / / / |
Reference Info. |
IEICE Tech. Rep., vol. 114, no. 326, NC2014-28, pp. 15-20, Nov. 2014. |
Paper # |
NC2014-28 |
Date of Issue |
2014-11-14 (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) |
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NC2014-28 |
Conference Information |
Committee |
MBE NC |
Conference Date |
2014-11-21 - 2014-11-22 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tohoku University |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
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Paper Information |
Registration To |
NC |
Conference Code |
2014-11-MBE-NC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Hyper-parameter estimation for compressive sensing with a Bernoulli-Gauss prior distribution |
Sub Title (in English) |
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Keyword(1) |
Compressive sensing |
Keyword(2) |
Statistical mechanics |
Keyword(3) |
Replica method |
Keyword(4) |
EM algorithm |
Keyword(5) |
Markov chain Monte Carlo method |
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1st Author's Name |
Toshiyuki Watanabe |
1st Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
2nd Author's Name |
Jun-ichi Inoue |
2nd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
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Speaker |
Author-1 |
Date Time |
2014-11-21 11:50:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
NC2014-28 |
Volume (vol) |
vol.114 |
Number (no) |
no.326 |
Page |
pp.15-20 |
#Pages |
6 |
Date of Issue |
2014-11-14 (NC) |
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