Unsupervised discovery of structure in activity data using multiple eigenspaces

  • Authors:
  • Tâm Huỳnh;Bernt Schiele

  • Affiliations:
  • Computer Science Department, TU Darmstadt, Germany;Computer Science Department, TU Darmstadt, Germany

  • Venue:
  • LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
  • Year:
  • 2006

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Abstract

In this paper we propose a novel scheme for unsupervised detection of structure in activity data. Our method is based upon an algorithm that represents data in terms of multiple low-dimensional eigenspaces. We describe the algorithm and propose an extension that allows to handle multiple time scales. The validity of the approach is demonstrated on several data sets and using two types of acceleration features. Finally, we report on experiments that indicate that our approach can yield recognition rates comparable to other, supervised approaches.