Dynamic Factorization Tests: Applications to Multi-modal Data Association

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Michael R. Siracusa, John W. Fisher III ;
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:508-515, 2007.

Abstract

The goal of a dynamic dependency test is to correctly label the interaction of multiple observed data streams and to describe how this interaction evolves over time. To this end, we propose the use of a hidden factorization Markov model (HFactMM) in which a hidden state indexes into a finite set of possible dependence structures on observations. We show that a dynamic dependency test using an HFactMM takes advantage of both structural and parametric changes associated with changes in interaction. This is contrasted both theoretically and empirically with standard sliding window based dependence analysis. Using this model we obtain state-ofthe-art performance on an audio-visual association task without the benefit of labeled training data.

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