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.
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Huỳnh, T., Schiele, B. (2006). Unsupervised Discovery of Structure in Activity Data Using Multiple Eigenspaces. In: Hazas, M., Krumm, J., Strang, T. (eds) Location- and Context-Awareness. LoCA 2006. Lecture Notes in Computer Science, vol 3987. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752967_11
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DOI: https://doi.org/10.1007/11752967_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34150-5
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