Date of Original Version



Conference Proceeding

Rights Management

Copyright 2012 by the authors

Abstract or Description

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.



Published In

Journal of Machine Learning Research : Workshop and Conference Proceedings, 22, 409-421.