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Hierarchical latent dictionaries for models of brain activation

Publication ,  Conference
Fyshe, A; Fox, E; Dunson, D; Mitchell, T
Published in: Journal of Machine Learning Research
January 1, 2012

In this work, we propose a hierarchical latent dictionary approach to estimate the timevarying mean and covariance of a process for which we have only limited noisy samples. We fully leverage the limited sample size and redundancy in sensor measurements by transferring knowledge through a hierarchy of lower dimensional latent processes. As a case study, we utilize Magnetoencephalography (MEG) recordings of brain activity to identify the word being viewed by a human subject. Specifically, we identify the word category for a single noisy MEG recording, when only given limited noisy samples on which to train.

Duke Scholars

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

January 1, 2012

Volume

22

Start / End Page

409 / 421

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences
 

Citation

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MLA
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Fyshe, A., Fox, E., Dunson, D., & Mitchell, T. (2012). Hierarchical latent dictionaries for models of brain activation. In Journal of Machine Learning Research (Vol. 22, pp. 409–421).
Fyshe, A., E. Fox, D. Dunson, and T. Mitchell. “Hierarchical latent dictionaries for models of brain activation.” In Journal of Machine Learning Research, 22:409–21, 2012.
Fyshe A, Fox E, Dunson D, Mitchell T. Hierarchical latent dictionaries for models of brain activation. In: Journal of Machine Learning Research. 2012. p. 409–21.
Fyshe, A., et al. “Hierarchical latent dictionaries for models of brain activation.” Journal of Machine Learning Research, vol. 22, 2012, pp. 409–21.
Fyshe A, Fox E, Dunson D, Mitchell T. Hierarchical latent dictionaries for models of brain activation. Journal of Machine Learning Research. 2012. p. 409–421.

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

January 1, 2012

Volume

22

Start / End Page

409 / 421

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences