Information-theoretic framework for unsupervised activity classification

Authors: Kaplan, Frédéric; Hafner, Verena V.

Source: Advanced Robotics, Volume 20, Number 10, 2006 , pp. 1087-1103(17)

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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

Document Type: Research article

DOI: http://dx.doi.org/10.1163/156855306778522514

Publication date: 2006-10-01