Hierarchical Latent Dictionaries for Models of Brain Activation

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.

• Type: Journal
• Year: 2012
• Where: JMLR
• Authors: Alona Fyshe, Emily B. Fox, David B. Dunson, Tom M. Mitchell
• Keywords: Dictionary Approach, JMLR 2012, Noisy Samples, Programming Languages, Sensor Measurements