Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Translation-invariant mixture models for curve clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Segmental Hidden Markov Models with Random Effects for Waveform Modeling
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Multi-task learning for HIV therapy screening
Proceedings of the 25th international conference on Machine learning
A reproducing kernel Hilbert space framework for pairwise time series distances
Proceedings of the 25th international conference on Machine learning
An Improved Multi-task Learning Approach with Applications in Medical Diagnosis
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Proceedings of the VLDB Endowment
Finding anomalous periodic time series
Machine Learning
Kernels for Periodic Time Series Arising in Astronomy
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Bayesian Online Multitask Learning of Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel methods and their application to structured data
Kernel methods and their application to structured data
Sparse gaussian processes for multi-task learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Learning output kernels for multi-task problems
Neurocomputing
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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.