2012/03/05 by Yuyang Wang, Wang, Yuyang, Roni Khardon +3
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Bayesian Methods and Mixture Models #Bayesian inference #Bayesian probability #Computer science #Dirichlet distribution #Dirichlet process #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian #Gaussian Processes and Bayesian Inference #Gaussian process #Inference #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Invariant (physics) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Mathematics #Mixture model #Model selection #Pattern recognition (psychology) #Series (stratigraphy) #Time Series Analysis and Forecasting #Time series #astro-ph.IM #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1203.0970
This is an extended version of our ECML 2010 paper entitled "Shift-invariant Grouped Multi-task Learning for Gaussian Processes"; ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
openalex publication_date 2012/03/05 · arxiv created 2013/05/20 · arxiv updated 2015/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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. 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.