PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Analyzing Movement Trajectories Using a Markov Bi-Clustering Method
Keren Erez, Jacob Goldberger, Ronen Sosnik, Moshe Shemesh and Moshe Abeles
Journal of Computational Neuroscience Volume 27, pp. 543-552, 2009. ISSN 0929-5313

Abstract

In this study we treat scribbling motion as a compositional system in which a limited set of elementary strokes are capable of concatenating amongst themselves in an endless number of combinations, thus producing an unlimited repertoire of complex constructs. We broke the continuous scribblings into small units and then calculated the Markovian transition matrix between the trajectory clusters. The Markov states are grouped in a way that minimizes the loss of mutual information between adjacent strokes. The grouping algorithm is based on a novel markov-state bi-clustering algorithm derived from the Information-Bottleneck principle. This approach hierarchically decomposes scribblings into increasingly finer elements. We illustrate the usefulness of this approach by applying it to human scribbling.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:5814
Deposited By:Jacob Goldberger
Deposited On:08 March 2010