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A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies

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Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8888))

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Abstract

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first-order Markov chain. In other words, only one-step back dependencies are modeled which is a rather unrealistic assumption in most applications. In this paper, we propose a method for postulating HMMs with approximately infinitely-long time-dependencies. Our approach considers the whole history of model states in the postulated dependencies, by making use of a recently proposed nonparametric Bayesian method for modeling label sequences with infinitely-long time dependencies, namely the sequence memoizer. We manage to derive training and inference algorithms for our model with computational costs identical to simple first-order HMMs, despite its entailed infinitely-long time-dependencies, by employing a mean-field-like approximation. The efficacy of our proposed model is experimentally demonstrated.

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Author information

Authors and Affiliations

  1. Cyprus University of Technology, Cyprus

    Sotirios P. Chatzis

  2. University of Patras, Greece

    Dimitrios I. Kosmopoulos

  3. Technological Educational Institute of Crete, Greece

    George M. Papadourakis

Authors
  1. Sotirios P. Chatzis
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  2. Dimitrios I. Kosmopoulos
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  3. George M. Papadourakis
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Sata Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Chatzis, S.P., Kosmopoulos, D.I., Papadourakis, G.M. (2014). A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-14364-4_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14363-7

  • Online ISBN: 978-3-319-14364-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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