Tensor Decomposition for Signal Processing and Machine Learning

Sidiropoulos, Nicholas D.; Lathauwer, Lieven De; Fu, Xiao; Huang, Kejun; Papalexakis, Evangelos E.; Faloutsos, Christos

doi:10.1109/tsp.2017.2690524

articleIEEE Transactions on Signal ProcessingApr 3, 2017GREEN OA

Tensor Decomposition for Signal Processing and Machine Learning

NDNicholas D. Sidiropoulos LDLieven De Lathauwer XFXiao Fu KHKejun Huang EEEvangelos E. Papalexakis

University of Minnesota · KU Leuven · +2 more institutions

Indexed inarxivcrossref

Abstract

Tensors or multiway arrays are functions of three or more indices (i, j, k, . . . )-similar to matrices (two-way arrays), which are functions of two indices (r, c) for (row, column). Tensors have a rich history, stretching over almost a century, and touching upon numerous disciplines; but they have only recently become ubiquitous in signal and data analytics at the confluence of signal processing, statistics, data mining, and machine learning. This overview article aims to provide a good starting point for researchers and practitioners interested in learning about and working with tensors. As such, it focuses on fundamentals and motivation (using various application examples), aiming to strike an appropriate…

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1,592

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FWCI: 70.69
Percentile: 100%
References: 174

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Topics & keywords

Topics

Keywords

Tensor (intrinsic definition)
Identifiability
Multilinear algebra
Multilinear map
Computer science
Matrix decomposition
Signal processing
Rank (graph theory)

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