Maximum Margin Temporal Clustering

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Abstract

Temporal Clustering (TC) refers to the factorization of multiple time series into a set of non-overlapping segments that belong to k temporal clusters. Existing methods based on extensions of generative models such as k-means or Switching Linear Dynamical Systems often lead to intractable inference and lack a mechanism for feature selection, critical when dealing with high dimensional data. To overcome these limitations, this paper proposes Maximum Margin Temporal Clustering (MMTC). MMTC simultaneously determines the start and the end of each segment, while learning a multi-class Support Vector Machine to discriminate among temporal clusters. MMTC extends Maximum Margin Clustering in two ways: first, it incorporates the notion of TC, and second, it introduces additional constraints to achieve better balance between clusters. Experiments on clustering human actions and bee dancing motions illustrate the benefits of our approach compared to state-of-the-art methods.

Citation

Minh Hoai and Fernando de la Torre
"Maximum Margin Temporal Clustering",
Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), 2012

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Acknowledgements and Funding

This work was supported by the National Science Foundation RI-1116583 and partially supported by the National Science Foundation under Grant CPS-0931999. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The authors would like to thank Zhen-Zhong Lan for helping with the experiments.

The first author, Minh Hoai, would like to dedicate his share of this work to Professor Nguyen Duy Tien on the occasion of his 70^th birthday, with highest regards and gratefulness.

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