%0 Journal Article
%T A new stochastic optimization algorithm to decompose large nonnegative tensors
%+ Laboratoire des Sciences de l'Information et des Systèmes (LSIS)
%+ Institut de Mathématiques de Marseille (I2M)
%+ TO Simulate and CAlibrate stochastic models (TOSCA)
%+ Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon) (LIS)
%+ Signal et Image (SIIM)
%+ Université de Toulon - École d’ingénieurs SeaTech (UTLN SeaTech)
%A Thanh Vu †, Xuan
%A Maire, Sylvain
%A Chaux, Caroline
%A Thirion-Moreau, Nadège
%< avec comité de lecture
%@ 1070-9908
%J IEEE Signal Processing Letters
%I Institute of Electrical and Electronics Engineers
%P 12 pp.
%8 2015
%D 2015
%K Candecomp/Parafac (CP) decomposi- tion
%K multi-linear algebra
%K Nonnegative Tensor Factorization (NTF)
%K stochastic optimization
%K missing data
%K Big data/tensors
%Z Computer Science [cs]/Signal and Image ProcessingJournal articles
%X In this letter, the problem of nonnegative tensor decompositions is addressed. Classically, this problem is carried out using iterative (either alternating or global) deterministic optimization algorithms. Here, a rather different stochastic approach is suggested. In addition, the ever-increasing volume of data requires the development of new and more efficient approaches to be able to process " Big data " tensors to extract relevant information. The stochastic algorithm outlined here comes within this framework. Both flexible and easy to implement, it is designed to solve the problem of the CP (Candecomp/Parafac) decomposition of huge nonnegative 3-way tensors while simultaneously enabling to handle possible missing data.
%G English
%2 https://hal.science/hal-01146443/document
%2 https://hal.science/hal-01146443/file/SingleFinalForIEEESPL.pdf
%L hal-01146443
%U https://hal.science/hal-01146443
%~ UNIV-TLN
%~ CNRS
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%~ UNIV-AMU
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%~ IECN
%~ EC-MARSEILLE
%~ INRIASO
%~ INRIA_TEST
%~ TESTALAIN1
%~ I2M
%~ I2M-2014-
%~ UNIV-LORRAINE
%~ INRIA2
%~ UNIV-COTEDAZUR
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