Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes

Details Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes

2012-03-05

arxiv.org/abs/1203.0970v1

Computer Science

Learning

This is an extended version of our ECML 2010 paper entitled "Shift-invariant Grouped Multi-task Learning for Gaussian Processe

Scientific paper

Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task is a phase-shifted periodic time series. In particular, we develop a novel Bayesian nonparametric model capturing a mixture of Gaussian processes where each task is a sum of a group-specific function and a component capturing individual variation, in addition to each task being phase shifted. We develop an efficient \textsc{em} algorithm to learn the parameters of the model. As a special case we obtain the Gaussian mixture model and \textsc{em} algorithm for phased-shifted periodic time series. Furthermore, we extend the proposed model by using a Dirichlet Process prior and thereby leading to an infinite mixture model that is capable of doing automatic model selection. A Variational Bayesian approach is developed for inference in this model. Experiments in regression, classification and class discovery demonstrate the performance of the proposed models using both synthetic data and real-world time series data from astrophysics. Our methods are particularly useful when the time series are sparsely and non-synchronously sampled.

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