Talks and Poster Presentations (with Proceedings-Entry):

M. Rupp, S. Schwarz:
"Gradient-Based Approaches To Learn Tensor Products";
Talk: 23rd European Signal Processing Conference (EUSIPCO-2015), Nice, France; 08-31-2015 - 09-04-2015; in: "EUSIPCO 2015", (2015), 2486 - 2490.

English abstract:
Tensor algebra has become of high interest recently due to its application
in the field of so-called Big Data. For signal processing
a first important step is to compress a vast amount of data into a
small enough set so that particular issues of interest can be investigated
with todays computer methods. We propose various gradientbased
methods to decompose tensors of matrix products as they appear
in structured multiple-input multiple-output systems. While
some methods work directly on the observed tensor, others use inputoutput
observations to conclude to the desired decomposition. Although
the algorithms are nonlinear in nature, they are being treated
as linear estimators; numerical examples validate our results.

Tensors, Decomposition, BigData

Electronic version of the publication:

Related Projects:
Project Head Markus Rupp:
Signal and Information Processing in Science and Engineering II: Theory and Implementation of Distributed Algorithms

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