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Citation
Michael Siracusa, John Fisher. "Dynamic Dependency Tests: Analysis and Applications to Multi-modal Data Association". Eleventh International Conference on Artificial Intelligence and Statistics', March, 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-of-the-art performance on an audio-visual association task without the benefit of labeled training data.

Electronic downloads

• siracusafisher-aistats07.pdf · application/pdf · 484 kbytes

• HTML

  Michael Siracusa, John Fisher. <a
  href="http://www.truststc.org/pubs/274.html"
  >Dynamic Dependency Tests: Analysis and Applications to
  Multi-modal Data Association</a>, Eleventh
  International Conference on Artificial Intelligence and
  Statistics', March, 2007.

• Plain text

  Michael Siracusa, John Fisher. "Dynamic Dependency
  Tests: Analysis and Applications to Multi-modal Data
  Association". Eleventh International Conference on
  Artificial Intelligence and Statistics', March, 2007.

• BibTeX

  @inproceedings{SiracusaFisher07_DynamicDependencyTestsAnalysisApplicationsToMultimodal,
      author = {Michael Siracusa and John Fisher},
      title = {Dynamic Dependency Tests: Analysis and
                Applications to Multi-modal Data Association},
      booktitle = {Eleventh International Conference on Artificial
                Intelligence and Statistics'},
      month = {March},
      year = {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-of-the-art
                performance on an audio-visual association task
                without the benefit of labeled training data.},
      URL = {http://www.truststc.org/pubs/274.html}
  }
  

Posted by Michael Siracusa on 24 Jul 2007.
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