Predictive Active Set Selection Methods for Gaussian Processes

Project website

Technical University of Denmark · DTU Informatics <<<

University of Copenhagen · Bioinformatics Centre <<<

... description


June 23, 2011

We propose an active set selection framework for Gaussian process classification for cases when the dataset is large enough to render its inference prohibitive. Our scheme consists of a two step alternating procedure of active set update rules and hyperparameter optimization based upon marginal likelihood maximization. The active set update rules rely on the ability of the predictive distributions of a Gaussian process classifier to estimate the relative contribution of a datapoint when being either included or removed from the model. This means that we can use it to include points with potentially high impact to the classifier decision process while removing those that are less relevant. We introduce two active set rules based on different criteria, the first one prefers a model with interpretable active set parameters whereas the second puts computational complexity first, thus a model with active set parameters that directly control its complexity. We also provide both theoretical and empirical support for our active set selection strategy being a good approximation of a full Gaussian process classifier. Our extensive experiments show that our approach can compete with state-of-the-art classification techniques with reasonable time complexity.

... software

Matlab package

june 23, 2011

Set of functions and demo scripts implementing PASS-GP. The functions are fairly optimized for speed and unfortunately the code is scarcely documented. The code is based on GPML toolbox, particularly on the covariance function definitions and the minimize routine for ML type II inference.


... references

Predictive Active Set Selection Methods for Gaussian Processes

June 23, 2011

Ricardo Henao and Ole Winther

Technical report.


PASS-GP: Predictive Active Set Selection for Gaussian Processes

August 29 - September 1, 2010

Ricardo Henao and Ole Winther

2010 IEEE International Workshop on Machine Learning for Signal Processing.

www pdf