Fast Nonparametric Clustering of Structured Time-Series

James Hensman; Magnus Rattray; Neil D. Lawrence

doi:10.1109/TPAMI.2014.2318711

edit

Back to publications

Fast Nonparametric Clustering of Structured Time-Series

James Hensman, Magnus Rattray, Neil D. Lawrence

IEEE Transactions on Pattern Analysis and Machine Intelligence, 37:383-393, 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.

Links

Cite this Paper

BibTeX


@Article{Hensman-fast14,
  title = 	 {Fast Nonparametric Clustering of Structured Time-Series},
  author = 	 {Hensman, James and Rattray, Magnus and Lawrence, Neil D.},
  journal =      {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  pages = 	 {383--393},
  year = 	 {2014},
  volume = 	 {37},
  doi = 	 {10.1109/TPAMI.2014.2318711},
  pdf = 	 {https://ieeexplore.ieee.org/ielx7/34/7004096/06802369.pdf},
  url = 	 {/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 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
%P 383--393
%R 10.1109/TPAMI.2014.2318711
%U /publications/hensman-fast14.html
%V 37
%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  - 2014/04/18	
ID  - Hensman-fast14
VL  - 37
SP  - 383
EP  - 393
DO  - 10.1109/TPAMI.2014.2318711
L1  - https://ieeexplore.ieee.org/ielx7/34/7004096/06802369.pdf
UR  - /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.. (2014). Fast Nonparametric Clustering of Structured Time-Series. IEEE Transactions on Pattern Analysis and Machine Intelligence 37:383-393 doi:10.1109/TPAMI.2014.2318711 Available from /publications/hensman-fast14.html.