Sparse Convolved Multiple Output Gaussian Processes

Details Sparse Convolved Multiple Output Gaussian Processes Sparse Convolved Multiple Output Gaussian Processes

2009-11-26

arxiv.org/abs/0911.5107v1

Statistics

Machine Learning

Extended version of the conference paper Sparse Convolved Gaussian Processes for Multi-output Regression (NIPS 2008)

Scientific paper

Recently there has been an increasing interest in methods that deal with multiple outputs. This has been motivated partly by frameworks like multitask learning, multisensor networks or structured output data. From a Gaussian processes perspective, the problem reduces to specifying an appropriate covariance function that, whilst being positive semi-definite, captures the dependencies between all the data points and across all the outputs. One approach to account for non-trivial correlations between outputs employs convolution processes. Under a latent function interpretation of the convolution transform we establish dependencies between output variables. The main drawbacks of this approach are the associated computational and storage demands. In this paper we address these issues. We present different sparse approximations for dependent output Gaussian processes constructed through the convolution formalism. We exploit the conditional independencies present naturally in the model. This leads to a form of the covariance similar in spirit to the so called PITC and FITC approximations for a single output. We show experimental results with synthetic and real data, in particular, we show results in pollution prediction, school exams score prediction and gene expression data.

Affiliated with

Also associated with

No associations

Rating

Sparse Convolved Multiple Output Gaussian Processes does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Sparse Convolved Multiple Output Gaussian Processes, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Sparse Convolved Multiple Output Gaussian Processes will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-116871