Project details for A Local and Parallel Computation Toolbox for Gaussian Process Regression

Logo A Local and Parallel Computation Toolbox for Gaussian Process Regression 1.0

by cwpark - March 19, 2012, 17:21:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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Description:

This toolbox (GPLP) is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method [1], partial independent conditional [2], localized probabilistic regression [3], and bagging for Gaussian process regression [4]. Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software.

This toolbox is implemented in Matlab code such that it is executable and has been tested in Matlab Version 7.7 or later versions, and Octave Version 3.2.4 or later versions. It might be executable in any of Matlab Version 7.x and any of Octave Version 3.2.x, but it has not been tested on those versions.

Refereces:

[1] Chiwoo Park, Jianhua Z. Huang, and Yu Ding. Domain decomposition approach for fast gaussian process regression of large spatial data sets. Journal of Machine Learning Research, 12:1697-1728, 2011.

[2] Edward Snelson and Zoubin Ghahramani. Local and global sparse Gaussian process approximations. In International Conference on Arti cal Intelligence and Statistics 11, pages 524-531. Society for Arti cial Intelligence and Statistics, 2007.

[3] Raquel Urtasun and Trevor Darrell. Sparse probabilistic regression for activity-independent human pose inference. In IEEE Conference on Computer Vision and Pattern Recognition 2008, pages 1-8, 2008.

[4] Tao Chen and Jianghong Ren. Bagging for Gaussian process regression. Neurocomputing, 72(7-9):1605-1610, 2009.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Platform Independent, Other
Data Formats: Matlab, Any Format Supported By Matlab
Tags: Regression, Domain Decomposition, Fast Computation, Gaussian Process, Localized Regression
Archive: download here

Comments

Chiwoo Park (on May 2, 2012, 21:24:38)

Acknowledgements

The authors gratefully acknowledge the support of the National Science Foundation through the grant CMMI-1000099/1000088.

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