Fast nonparametric clustering of structured time-series

James HensmanMagnus RattrayNeil D. Lawrence
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014.

Abstract

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.

Cite this Paper


BibTeX
@Article{Hensman-fast14, title = {Fast nonparametric clustering of structured time-series}, author = {James Hensman and Magnus Rattray and Neil D. Lawrence}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2014}, doi = {10.1109/TPAMI.2014.2318711}, abstract = {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.} }
Endnote
%0 Journal Article %T Fast nonparametric clustering of structured time-series %A James Hensman %A Magnus Rattray %A Neil D. Lawrence %J IEEE Transactions on Pattern Analysis and Machine Intelligence %D 2014 %F Hensman-fast14 %R 10.1109/TPAMI.2014.2318711 %X 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.
RIS
TY - JOUR TI - Fast nonparametric clustering of structured time-series AU - James Hensman AU - Magnus Rattray AU - Neil D. Lawrence DA - 2014/01/01 ID - Hensman-fast14 DO - 10.1109/TPAMI.2014.2318711 AB - 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. ER -
APA
Hensman, J., Rattray, M. & Lawrence, N.D.. (2014). Fast nonparametric clustering of structured time-series. IEEE Transactions on Pattern Analysis and Machine Intelligence doi:10.1109/TPAMI.2014.2318711

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