#  STAT 300: Finale Doshi-Velez 

 



####  calendar\_today Date and Time 

 **October 25, 2016** 

 12:00PM - 01:00PM EDT 

####  pin\_drop 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.



 

 



 

 See also:- [ 2016 - 2017 ](/academic-year/2016-2017)
 
 

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