Seminar Details
2010-02-09 (14.00) : Numerical methods for the best low multilinear rank approximation of higher-order tensors.
At Euler building
Organized by Mathematical Engineering
Speaker :
Ishteva, Mariya
Abstract :
Higher-order tensors are generalizations of vectors and matrices to third-
or even higher-order arrays of numbers. The multilinear rank of a tensor
is a direct generalization of column and row rank of a matrix. In this
talk we discuss the best low multilinear rank approximation. Given a
higher-order tensor, we are looking for another tensor, as close as
possible to the original one and with multilinear rank bounded by
prespecified numbers. This approximation is used for dimensionality
reduction and signal subspace estimation in higher-order statistics,
biomedical signal processing, telecommunications and many other fields. We
consider two particular applications, present several algorithms for the
computation of the approximation and discuss the issue of local minima.