Departments of Computer Science and Applied Mathematics
Random Sampling in Massive Data Matrices and Tensors
The talk will survey a body of recently developed results to deal with
massive data in the form of matrices and tensors that arise in many modern
applications. The general gist is that if one picks a sub-matrix of
the input matrix at random. then indeed Linear Algebra can be carried
out on the random sample instead of the whole matrix. The key will be
a simple, judicious choice of probability distribution to do the random
sampling. The methods are extended to tensors. Besides the tradtional
applications such as Principal Component Analysis, these results can
also be used to solve approximately a class of combinatorial optimization problems.
Friday, October 20, 2006
Biotechnology Auditorium - 2:45 p.m. to 3:45 p.m.