Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms.
In this paper, we propose to mine brain medical publications to learn the spatial distribution associated with anatomical terms.
Approximate inference via information projection has been recently introduced as a general-purpose approach for efficient probabilistic inference given sparse variables.
Given two sets of variables, derived from a common set of samples, sparse Canonical Correlation Analysis (CCA) seeks linear combinations of a small number of variables in each set, such that the induced canonical variables are maximally correlated.
In cases where this projection is intractable, we propose a family of parameterized approximations indexed by subsets of the domain.
The l1-regularized Gaussian maximum likelihood estimator (MLE) has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix even under high-dimensional settings.