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publications
| Information-theoretic framework for unsupervised activity classification |
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Kaplan, F. and Hafner, V. V. (2006), Information-theoretic framework for unsupervised activity classification, Advanced Robotics, 20 (10) : 1087-1103 |
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Kaplan, F. and Hafner, V. V. (2006), Information-theoretic framework for unsupervised activity classification, Advanced Robotics, 20 (10) : 1087-1103
Ingentia Connect: (article)
Abstract:
This article presents a mathematical framework based on information
theory to compare multivariate sensory streams. Central to this
approach is the notion of configuration: a set of distances between
information sources, statistically evaluated for a given time span. As
information distances capture simultaneously effects of physical
closeness, intermodality, functional relationship and external
couplings, a configuration can be interpreted as a signature for
specific patterns of activity. This provides ways for comparing
activity sequences by viewing them as points in an activity space.
Results of experiments with an autonomous robot illustrate how this
framework can be used to perform unsupervised activity classification.
Keywords: ACTIVITY CLASSIFICATION; INFORMATION METRICS; UNSUPERVISED CLUSTERING |
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