Title :
Tensors versus matrices usefulness and unexpected properties
Author_Institution :
13S Lab., Univ. of Nice - Sophia Antipolis, Sophia Antipolis, France
Abstract :
Since the nineties, tensors are increasingly used in Signal Processing and Data Analysis. There exist striking differences between tensors and matrices, some being advantages, and others raising difficulties. These differences are pointed out in this paper while briefly surveying the state of the art. The conclusion is that tensors are omnipresent in real life, implicitly or explicitly, and must be used even if we still know quite little about their properties.
Keywords :
matrix algebra; signal processing; tensors; Canonical decomposition; Tensor rank; blind identification; data analysis; factor analysis; high-order statistics; low-rank approximation; matrix; signal processing; tensors; Conferences; Contracts; Data analysis; Laboratories; Matrix decomposition; Signal processing; Statistical analysis; Statistics; Tensile stress; Vectors; Blind identification; Canonical decomposition; Deflation; Factor analysis; High-order statistics; Low-rank approximation; Parafac; Separation of variables; Tensor rank;
Conference_Titel :
Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on
Conference_Location :
Cardiff
Print_ISBN :
978-1-4244-2709-3
Electronic_ISBN :
978-1-4244-2711-6
DOI :
10.1109/SSP.2009.5278471