Fast nonparametric clustering of structured time-series
This work addresses clustering challenges in biological time-series data, offering incremental improvements in modeling and computational efficiency.
The authors tackled the problem of clustering structured time-series data by combining Gaussian Process and Dirichlet Process models with innovations in the prior and inference, resulting in a model that better captures data features and achieves a twofold speed-up in inference.
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.