articles available

[Robert Jacobs]

The following papers may be of interest to readers of this
mailing list:

(1) Jacobs, R.A., Jiang, W., and Tanner, M.A. (2002) Factorial
     hidden Markov models and the generalized backfitting algorithm.
     Neural Computation, 14, 2415-2437.

(2) Jacobs, R.A. (2002) What determines visual cue reliability?
     Trends in Cognitive Sciences, 6, 345-350.

Robbie Jacobs

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(1) Jacobs, R.A., Jiang, W., and Tanner, M.A. (2002) Factorial
     hidden Markov models and the generalized backfitting algorithm.
     Neural Computation, 14, 2415-2437.

     Previous researchers developed new learning architectures for
     sequential data by extending conventional hidden Markov models
     through the use of distributed state representations.  Although
     exact inference and parameter estimation in these architectures
     is computationally intractable, Ghahramani and Jordan (1997)
     showed that approximate inference and parameter estimation in
     one such architecture, factorial hidden Markov models (FHMMs),
     is feasible in certain circumstances.  However, the learning
     algorithm proposed by these investigators, based on variational
     techniques, is difficult to understand and implement, and
     is limited to the study of real-valued datasets.  This paper
     proposes an alternative method for approximate inference and
     parameter estimation in FHMMs based on the perspective that FHMMs
     are a generalization of a well-known class of statistical models
     known as Generalized Additive Models (GAMs; Hastie and Tibshirani,
     1990).  Using existing statistical techniques for GAMs as a guide,
     we have developed the generalized backfitting algorithm.  This
     algorithm computes customized error signals for each hidden
     Markov chain of an FHMM, and then trains each chain one at a
     time using conventional techniques from the hidden Markov models
     literature.  Relative to previous perspectives on FHMMs, we
     believe that the viewpoint taken here has a number of advantages.
     First, it places FHMMs on firm statistical foundations by relating
     FHMMs to a class of models that are well-studied in the statistics
     community, yet it generalizes this class of models in an
     interesting way.  Second, it leads to an understanding of how FHMMs
     can be applied to many different types of time series data,
     including Bernoulli and multinomial data, not just data which are
     real-valued.  Lastly, it leads to an effective learning procedure
     for FHMMs which is easier to understand and easier to implement
     than existing learning procedures.  Simulation results suggest that
     FHMMs trained with the generalized backfitting algorithm are a
     practical and powerful tool for analyzing sequential data.

     http://www.bcs.rochester.edu/people/robbie/jacobs.j.t.nc02.pdf

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(2) Jacobs, R.A. (2002) What determines visual cue reliability?
     Trends in Cognitive Sciences, 6, 345-350.

     Visual environments often contain many cues to properties of an
     observed scene.  In order to integrate information provided by
     multiple cues in an efficient manner, observers must assess the
     degree to which each cue provides reliable versus unreliable
     information.  Two hypotheses are reviewed regarding how observers
     estimate cue reliabilities, namely that the estimated reliability
     of a cue is related to the ambiguity of the cue, and that people
     use correlations among cues in order to estimate cue reliabilities.
     It is shown that cue reliabilities are important both for cue
     combination and for aspects of visual learning.

     http://www.bcs.rochester.edu/people/robbie/jacobs.tics02.pdf


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Robert Jacobs
Department of Brain and Cognitive Sciences
University of Rochester
Rochester, NY 14627-0268
phone: 585-275-0753
fax: 585-442-9216
email: robbie at bcs.rochester.edu
web: http://www.bcs.rochester.edu/people/robbie/robbie.html