Seminar Details
2020-05-05 (14:00) : Identifiability and Detection of Multiset Correlation Structure
At MS Teams
Organized by Mathematical Engineering
Speaker :
Marrinan, Tim
Abstract :
Relationships between random vectors with deterministic but unknown linear mixing are commonly assessed via canonical correlations. Detecting this correlation structure allows us to infer unseen associations between the underlying variables. For example, this type of analysis has been used to identify conditions that lead to a rise in sea surface temperature and to localize regions of brain activity used for visual recognition tasks. For pairs of random vectors, general conditions have been discovered under which the correlation structure is identifiable, but for collections of more than two random vectors such general conditions remain evasive. In this lecture we describe conditions under which a subset of multiset correlation structures can be identified, and demonstrate how the correlation structures can be detected from the augmented coherence matrix of the data.