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arXiv · 1401.1605

Fast nonparametric clustering of structured time-series

James Hensman·Magnus Rattray·Neil D. Lawrence
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Abstract

In this publication, we combine two Bayesian non-parametric 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 variationala pproximation 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 twofold speed-up over EM-based variational inference.

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James Hensman, Magnus Rattray, Neil D. Lawrence. 2014-01-08. Fast nonparametric clustering of structured time-series. https://arxiv.org/abs/1401.1605

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