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Fast nonparametric clustering of structured time-series

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Fast nonparametric clustering of structured time-series. / Hensman, James; Rattray, Magnus; Lawrence, Neil D.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 2, 6802369, 01.02.2015, p. 383-393.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Hensman, J, Rattray, M & Lawrence, ND 2015, 'Fast nonparametric clustering of structured time-series', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 2, 6802369, pp. 383-393. https://doi.org/10.1109/TPAMI.2014.2318711

APA

Hensman, J., Rattray, M., & Lawrence, N. D. (2015). Fast nonparametric clustering of structured time-series. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(2), 383-393. Article 6802369. https://doi.org/10.1109/TPAMI.2014.2318711

Vancouver

Hensman J, Rattray M, Lawrence ND. Fast nonparametric clustering of structured time-series. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015 Feb 1;37(2):383-393. 6802369. Epub 2014 Apr 18. doi: 10.1109/TPAMI.2014.2318711

Author

Hensman, James ; Rattray, Magnus ; Lawrence, Neil D. / Fast nonparametric clustering of structured time-series. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015 ; Vol. 37, No. 2. pp. 383-393.

Bibtex

@article{9fde2b78307f4ee1b2ae527a8a930d8c,
title = "Fast nonparametric clustering of structured time-series",
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.",
keywords = "Gaussian processes, Data models, Time series analysis, Biological system modeling, Computational modeling, Optimization, Vectors",
author = "James Hensman and Magnus Rattray and Lawrence, {Neil D.}",
year = "2015",
month = feb,
day = "1",
doi = "10.1109/TPAMI.2014.2318711",
language = "English",
volume = "37",
pages = "383--393",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
issn = "0162-8828",
publisher = "IEEE Computer Society",
number = "2",

}

RIS

TY - JOUR

T1 - Fast nonparametric clustering of structured time-series

AU - Hensman, James

AU - Rattray, Magnus

AU - Lawrence, Neil D.

PY - 2015/2/1

Y1 - 2015/2/1

N2 - 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.

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.

KW - Gaussian processes

KW - Data models

KW - Time series analysis

KW - Biological system modeling

KW - Computational modeling

KW - Optimization

KW - Vectors

U2 - 10.1109/TPAMI.2014.2318711

DO - 10.1109/TPAMI.2014.2318711

M3 - Journal article

AN - SCOPUS:84920982448

VL - 37

SP - 383

EP - 393

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

SN - 0162-8828

IS - 2

M1 - 6802369

ER -