Shift-Invariant Grouped Multi-Task Learning for Gaussian Processes
Abstract
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 em algorithm to learn the parameters of the model. As a special case we obtain the Gaussian mixture model and em algorithm for phased-shifted periodic time series. Experiments in regression, classification and class discovery demonstrate the performance of the proposed model 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.
Cite
Text
Wang et al. "Shift-Invariant Grouped Multi-Task Learning for Gaussian Processes." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15939-8_27Markdown
[Wang et al. "Shift-Invariant Grouped Multi-Task Learning for Gaussian Processes." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/wang2010ecmlpkdd-shiftinvariant/) doi:10.1007/978-3-642-15939-8_27BibTeX
@inproceedings{wang2010ecmlpkdd-shiftinvariant,
title = {{Shift-Invariant Grouped Multi-Task Learning for Gaussian Processes}},
author = {Wang, Yuyang and Khardon, Roni and Protopapas, Pavlos},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {418-434},
doi = {10.1007/978-3-642-15939-8_27},
url = {https://mlanthology.org/ecmlpkdd/2010/wang2010ecmlpkdd-shiftinvariant/}
}