Presentation | 2014-11-21 Hyper-parameter estimation for compressive sensing with a Bernoulli-Gauss prior distribution Toshiyuki WATANABE, Jun-ichi INOUE, |
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Abstract(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) |
Keyword(in English) | Compressive sensing / Statistical mechanics / Replica method / EM algorithm / Markov chain Monte Carlo method |
Paper # | NC2014-28 |
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Committee | NC |
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Conference Date | 2014/11/14(1days) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
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) | |
Keyword(1) | Compressive sensing |
Keyword(2) | Statistical mechanics |
Keyword(3) | Replica method |
Keyword(4) | EM algorithm |
Keyword(5) | Markov chain Monte Carlo method |
1st Author's Name | Toshiyuki WATANABE |
1st Author's Affiliation | Graduate School of Information Science and Technology, Hokkaido University /() |
2nd Author's Name | Jun-ichi INOUE |
2nd Author's Affiliation | |
Date | 2014-11-21 |
Paper # | NC2014-28 |
Volume (vol) | vol.114 |
Number (no) | 326 |
Page | pp.pp.- |
#Pages | 6 |
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