Presentation | 2012-11-07 Fast Gaussian Process Regression using hash function Yuya OKADOME, Yutaka NAKAMURA, Hiroshi ISHIGURO, |
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Abstract(in Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Gaussian Process Regression / Locality-Sensitive Hashing / Product of Experts model |
Paper # | IBISML2012-55 |
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Committee | IBISML |
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Conference Date | 2012/10/31(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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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 |