Tuesday, 13:15 - 13:40 h, Room: H 3004

 

Stephen Vavasis
Identifying k large submatrices using convex programming

Coauthors: Venkat Chandrasekaran, Xuan Vinh Doan

 

Abstract:
We consider the problem of identifying k large approximately rank-one submatrices of a nonnegative data matrix. Stated in a certain manner, this problem is NP-hard, but has important applications in data mining. In particular it is a version of the well-known nonnegative matrix factorization, which has been applied to document classification, image decomposition, and analysis of biochemical experiments. We prove that if the data is constructed according to a certain randomized model, then the k blocks can be recovered in polynomial time via convex relaxation.

 

Talk 1 of the invited session Tue.2.H 3004
"Cone factorizations and lifts of convex sets" [...]
Cluster 2
"Combinatorial optimization" [...]

 

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