Hierarchical latent dictionaries for models of brain activation
Conference Paper
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 Authors
Cited Authors
- Fyshe, A; Fox, E; Dunson, D; Mitchell, T
Published Date
- January 1, 2012
Published In
Volume / Issue
- 22 /
Start / End Page
- 409 - 421
Electronic International Standard Serial Number (EISSN)
- 1533-7928
International Standard Serial Number (ISSN)
- 1532-4435
Citation Source
- Scopus