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Unsupervised Discovery of Structure in Activity Data Using Multiple Eigenspaces

  • Tâm Huỳnh
  • Bernt Schiele
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3987)

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.

Keywords

Activity Recognition Reconstruction Error Inertial Sensor Acceleration Data Segment Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tâm Huỳnh
    • 1
  • Bernt Schiele
    • 1
  1. 1.Computer Science DepartmentTU DarmstadtGermany

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