Theory Seminar

Scalable Sparse Approximation of a Sample Mean

Clayton Scott

Associate Professor
University of Michigan
Friday, December 06, 2013
10:30am - 11:30am
BBB 3941

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About the Event

We examine the problem of approximating the mean of a set of vectors as a sparse linear combination of those vectors. This problem is motivated by a common methodology in machine learning where a probability distribution is represented as the sample mean of kernel functions. A sparse approximation of this kernel mean function is desirable in applications where the function must be evaluated efficiently. However, existing sparse approximation algorithms such as matching and basis pursuit scale quadratically in the sample size, and therefore do not scale well. We introduce an incoherence-based approximation bound, and examine bound minimization as a sparse approximation strategy. In the context of sparsely approximating a kernel mean function, the bound is efficiently minimized by solving an appropriate instance of the k-center problem, and the resulting algorithm has linear complexity in the sample size.

Additional Information

Sponsor(s): EECS

Open to: UM Only