NDLI: A Randomized Block Sampling Approach to Canonical Polyadic Decomposition of Large-Scale Tensors

Content Provider	IEEE Xplore Digital Library
Author	Vervliet, N. De Lathauwer, L.
Copyright Year	2007
Abstract	For the analysis of large-scale datasets one often assumes simple structures. In the case of tensors, a decomposition in a sum of rank-1 terms provides a compact and informative model. Finding this decomposition is intrinsically more difficult than its matrix counterpart. Moreover, for large-scale tensors, computational difficulties arise due to the curse of dimensionality. The randomized block sampling canonical polyadic decomposition method presented here combines increasingly popular ideas from randomization and stochastic optimization to tackle the computational problems. Instead of decomposing the full tensor at once, updates are computed from small random block samples. Using step size restriction the decomposition can be found up to near optimal accuracy, while reducing the computation time and number of data accesses significantly. The scalability is illustrated by the decomposition of a synthetic 8 TB tensor and a real life 12.5 GB tensor in a few minutes on a standard laptop.
Sponsorship	IEEE Signal Processing Society
Starting Page	284
Ending Page	295
Page Count	12
File Size	1995017
File Format	PDF
ISSN	19324553
Volume Number	10
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2016-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Signal processing algorithms Optimization Indexes Matrix decomposition Approximation algorithms Approximation methods blind source separation Tensor decomposition canonical polyadic decomposition CANDECOMP/PARAFAC randomized algorithms block sampling big data
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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