Maximum Margin Temporal Clustering

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 (SLDS) 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 multiclass Support Vector Machine (SVM) 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.