Name
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 \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. |
Year | Venue | Field |
|---|---|---|
2012 | CoRR | Time series,Dirichlet process,Multi-task learning,Computer science,Model selection,Synthetic data,Gaussian process,Artificial intelligence,Machine learning,Mixture model,Bayesian probability |
DocType | Volume | Citations |
Journal | abs/1203.0970 | 0 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuyang Wang | 1 | 45 | 9.73 |
| Roni Khardon | 2 | 1068 | 133.16 |
| Pavlos Protopapas | 3 | 127 | 14.73 |