Hierarchical Latent Dictionaries for Models of Brain Activation
Abstract
In this work, we propose a hierarchical latent dictionary approach to estimate the time-varying 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 given only limited noisy samples on which to train.
Cite
Text
Fyshe et al. "Hierarchical Latent Dictionaries for Models of Brain Activation." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.Markdown
[Fyshe et al. "Hierarchical Latent Dictionaries for Models of Brain Activation." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.](https://mlanthology.org/aistats/2012/fyshe2012aistats-hierarchical/)BibTeX
@inproceedings{fyshe2012aistats-hierarchical,
title = {{Hierarchical Latent Dictionaries for Models of Brain Activation}},
author = {Fyshe, Alona and Fox, Emily and Dunson, David and Mitchell, Tom},
booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics},
year = {2012},
pages = {409-421},
volume = {22},
url = {https://mlanthology.org/aistats/2012/fyshe2012aistats-hierarchical/}
}