Fast nonparametric clustering of structured time-series

James HensmanMagnus RattrayNeil D. Lawrence
, 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
@InProceedings{pmlr-v-hensman-fast14, title = {Fast nonparametric clustering of structured time-series}, author = {James Hensman and Magnus Rattray and Neil D. Lawrence}, year = {}, editor = {}, url = {http://inverseprobability.com/publications/hensman-fast14.html}, 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 Conference Paper %T Fast nonparametric clustering of structured time-series %A James Hensman %A Magnus Rattray %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-hensman-fast14 %I PMLR %J Proceedings of Machine Learning Research %P -- %R 10.1109/TPAMI.2014.2318711 %U http://inverseprobability.com %V %W PMLR %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 - CPAPER TI - Fast nonparametric clustering of structured time-series AU - James Hensman AU - Magnus Rattray AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-hensman-fast14 PB - PMLR SP - DP - PMLR EP - DO - 10.1109/TPAMI.2014.2318711 L1 - UR - http://inverseprobability.com/publications/hensman-fast14.html 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.. (). Fast nonparametric clustering of structured time-series. , in PMLR :-

Related Material