Speaker: Abhishek Kumar (IBM TJ Watson Research Center) Title: Topics in Nonnegative Matrix Factorization: Separable NMF and Semi-NMF Date and time: Jan 4, 2016 (3:00-4:30pm) Venue: RM Building, Room 101 Abstract: The goal in nonnegative matrix factorization (NMF) is to express, exactly or approximately, a given matrix as a product of two nonnegative matrices of smaller inner dimension. NMF and its variants have been widely used for extracting interpretable features and patterns in various applications, including text, vision, and speech. Computing NMF has been shown to be NP-hard. In the first part, I will talk about fast conical hull algorithms for NMF under the so-called separability assumption which makes the NMF problem tractable. I will show applications of separable NMF to the problem of Video foreground-background separation, comparing it with Robust PCA which is a widely used method for this problem. In the second part, I will talk about a related problem of Semi-nonnegative matrix factorization where only one of the factors is constrained to be nonnegative. I will talk about conditions for tractability, and exact and heuristic algorithms for computing Semi-NMF. This talk is based on joint works with Vikas Sindhwani and Nicolas Gillis. Speaker bio: Abhishek Kumar is a Research Staff Member at IBM TJ Watson Research Center with research interests broadly lying in the area of machine learning, with work ranging from learning from multiple views of data, multitask learning, and efficient non-negative matrix factorization algorithms (http://www.umiacs.umd.edu/~abhishek/index.html). Before joining IBM, he graduated with a Ph.D. from Department of Computer Science at the University of Maryland in 2013.