Presentation 2012-11-07
Fast Gaussian Process Regression using hash function
Yuya OKADOME, Yutaka NAKAMURA, Hiroshi ISHIGURO,
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Abstract(in English) A Gaussian Process Regression (GPR) has ability to deal with a non-linear regression easily. However, the calculation cost increase as the sample size. Local GPR proposed in a recent year performs fast calculation for a GPR by dividing a dataset and constructing local models in each divided subspace. However, the integration procedure of local models is not based on a the original probabilistic model. In this paper, we propose a fast calculation method for a GPR by using a Locally-Sensitive Hashing and a Product of Experts model. We apply our proposed method to function approximation of an artificial data, and confirm the effect of reduction of the calculation cost.
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Keyword(in English) Gaussian Process Regression / Locality-Sensitive Hashing / Product of Experts model
Paper # IBISML2012-55
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Committee IBISML
Conference Date 2012/10/31(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Fast Gaussian Process Regression using hash function
Sub Title (in English)
Keyword(1) Gaussian Process Regression
Keyword(2) Locality-Sensitive Hashing
Keyword(3) Product of Experts model
1st Author's Name Yuya OKADOME
1st Author's Affiliation Department of Engineering Science, Osaka Unaiversity()
2nd Author's Name Yutaka NAKAMURA
2nd Author's Affiliation Department of Engineering Science, Osaka Unaiversity
3rd Author's Name Hiroshi ISHIGURO
3rd Author's Affiliation Department of Engineering Science, Osaka Unaiversity
Date 2012-11-07
Paper # IBISML2012-55
Volume (vol) vol.112
Number (no) 279
Page pp.pp.-
#Pages 5
Date of Issue