Departments of Computer Science and Applied Mathematics
Yale University
http://www.cs.yale.edu/homes/kannan/home.html

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.