Date:
Tuesday, October 25, 2016, 12:00pm to 1:00pm
Location:
Science Center Rm. 705
Non-identifiability and Posterior Exploration in Non-negative Matrix Factorization
Non-negative matrix factorization (NMF) is a popular model for data exploration: each data point can be thought of as a convex, linear combination of a set of bases, and the bases represent something important about the structure of the data. I will first talk about non-identifiability in NMF -- which can thwart interpretation -- including some counter-intuitive examples of how factorizations may not be unique. Next, I will describe on-going work in my group on how to combine some of these NMF-specific insights with some very general techniques for rapidly exploring the space of probable solutions.