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2012
IEEE

Group action induced distances for averaging and clustering Linear Dynamical Systems with applications to the analysis of dynami

9 years 2 months ago
Group action induced distances for averaging and clustering Linear Dynamical Systems with applications to the analysis of dynami
We introduce a framework for defining a distance on the (non-Euclidean) space of Linear Dynamical Systems (LDSs). The proposed distance is induced by the action of the group of orthogonal matrices on the space of statespace realizations of LDSs. This distance can be efficiently computed for large-scale problems, hence it is suitable for applications in the analysis of dynamic visual scenes and other high dimensional time series. Based on this distance we devise a simple LDS averaging algorithm, which can be used for classification and clustering of time-series data. We test the validity as well as the performance of our groupaction based distance on synthetic as well as real data and provide comparison with state-of-the-art methods.
Bijan Afsari, Rizwan Chaudhry, Avinash Ravichandra
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Bijan Afsari, Rizwan Chaudhry, Avinash Ravichandran, René Vidal
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