Learning Time Series Segmentation Models from Temporally Imprecise Labels

Roy Adams, Benjamin M. Marlin
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:134-143, 2018.

Abstract

This paper considers the problem of learning time series segmentation models when the la- beled data are subject to temporal uncertainty or noise. Our approach augments the semi- Markov conditional random field (semi-CRF) model with a probabilistic model of the label observation process. This augmentation allows us to estimate the parameters of the semi-CRF from timestamps corresponding roughly to the occurrence of transitions between segments. We show how exact marginal inference can be performed in the augmented model in polyno- mial time, enabling learning based on marginal likelihood maximization. Our experiments on two activity detection problems show that the proposed approach can learn models from tem- porally imprecise labels, and can successfully refine imprecise segmentations through poste- rior inference. Finally, we show how inference complexity can be reduced by a factor of 40 using static and model-based pruning of the inference dynamic program.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-adams18a, title = {Learning Time Series Segmentation Models from Temporally Imprecise Labels}, author = {Adams, Roy and Marlin, Benjamin M.}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {134--143}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/adams18a/adams18a.pdf}, url = {https://proceedings.mlr.press/r16/adams18a.html}, abstract = {This paper considers the problem of learning time series segmentation models when the la- beled data are subject to temporal uncertainty or noise. Our approach augments the semi- Markov conditional random field (semi-CRF) model with a probabilistic model of the label observation process. This augmentation allows us to estimate the parameters of the semi-CRF from timestamps corresponding roughly to the occurrence of transitions between segments. We show how exact marginal inference can be performed in the augmented model in polyno- mial time, enabling learning based on marginal likelihood maximization. Our experiments on two activity detection problems show that the proposed approach can learn models from tem- porally imprecise labels, and can successfully refine imprecise segmentations through poste- rior inference. Finally, we show how inference complexity can be reduced by a factor of 40 using static and model-based pruning of the inference dynamic program.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Learning Time Series Segmentation Models from Temporally Imprecise Labels %A Roy Adams %A Benjamin M. Marlin %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-adams18a %I PMLR %P 134--143 %U https://proceedings.mlr.press/r16/adams18a.html %V R16 %X This paper considers the problem of learning time series segmentation models when the la- beled data are subject to temporal uncertainty or noise. Our approach augments the semi- Markov conditional random field (semi-CRF) model with a probabilistic model of the label observation process. This augmentation allows us to estimate the parameters of the semi-CRF from timestamps corresponding roughly to the occurrence of transitions between segments. We show how exact marginal inference can be performed in the augmented model in polyno- mial time, enabling learning based on marginal likelihood maximization. Our experiments on two activity detection problems show that the proposed approach can learn models from tem- porally imprecise labels, and can successfully refine imprecise segmentations through poste- rior inference. Finally, we show how inference complexity can be reduced by a factor of 40 using static and model-based pruning of the inference dynamic program. %Z Reissued by PMLR on 04 October 2026.
APA
Adams, R. & Marlin, B.M.. (2018). Learning Time Series Segmentation Models from Temporally Imprecise Labels. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:134-143 Available from https://proceedings.mlr.press/r16/adams18a.html. Reissued by PMLR on 04 October 2026.

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