M. Rupp,S. Schwarz:

"A Tensor LMS Algorithm";

Talk: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane; 04-19-2015 - 04-24-2015; in: "ICASSP 2015", (2015), 3347 - 3351.

Although the LMS algorithm is often preferred in practice due to

its numerous positive implementation properties, once the parameter

space to estimate becomes large, the algorithm suffers of slow

learning. Many ideas have been proposed to introduce some a-priori

knowledge into the algorithm to speed up its learning rate. Recently

also sparsity concepts have become of interest for such algorithms.

In this contribution we follow a different path by focusing on the

separability of linear operators, a typical property of interest when

dealing with tensors. Once such separability property is given, a

gradient type algorithm can be derived with significant increase in

learning rate. Even if separability is only given to a certain extent,

we show that the algorithm can still provide gains. We derive quality

and quantity measures to describe the algorithmic behavior in such

contexts and evaluate its properties by Monte Carlo simulations.

Tensor, LMS algorithm, Separability

http://publik.tuwien.ac.at/files/PubDat_239375.pdf

Project Head Markus Rupp:

Signal and Information Processing in Science and Engineering II: Theory and Implementation of Distributed Algorithms

Created from the Publication Database of the Vienna University of Technology.