Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models

Part of the Advances in Pattern Recognition book series (ACVPR)


We present a general differential-geometric framework for learning distance functions for dynamical models. Given a training set of models, the optimal metric is selected among a family of pullback metrics induced by the Fisher information tensor through a parameterized automorphism. The problem of classifying motions, encoded as dynamical models of a certain class, can then be posed on the learnt manifold. In particular, we consider the class of multidimensional autoregressive models of order 2. Experimental results concerning identity recognition are shown that prove how such optimal pullback Fisher metrics greatly improve classification performances.


Riemannian Manifold Hide Markov Model Classification Performance Autoregressive Model Geodesic Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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