Fast nonparametric clustering of structured time-series

Hensman, James and Rattray, Magnus and Lawrence, Neil D. (2015) Fast nonparametric clustering of structured time-series. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (2). pp. 383-393. ISSN 0162-8828

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In this publication, we combine two Bayesian nonparametric models: the Gaussian Process (GP) and the Dirichlet Process (DP). Our innovation in the GP model is to introduce a variation on the GP prior which enables us to model structured time-series data, i.e., data containing groups where we wish to model inter- and intra-group variability. Our innovation in the DP model is an implementation of a new fast collapsed variational inference procedure which enables us to optimize our variational approximation significantly faster than standard VB approaches. In a biological time series application we show how our model better captures salient features of the data, leading to better consistency with existing biological classifications, while the associated inference algorithm provides a significant speed-up over EM-based variational inference.

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Journal Article
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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27 Jan 2017 14:40
Last Modified:
15 Mar 2022 03:22