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Non-negative sub-tensor ensemble factorization (NsTEF) algorithm. A new incremental tensor factorization for large data sets.

Vincent Vigneron 1 Andreas Kodewitz 1 Michele Nazareth da Costa 2 Ana Maria Tome 3 Elmar Langlang 4
1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
2 DSPCom - Digital Signal Processing and Communications laboratory [Campinas]
3 IEETA - Institute of Electronics and Telematics Engineering of Aveiro
4 Institut für Biophysik und physikalische Biochemie
Abstract : In this work we present a novel algorithm for nonnegative tensor factorization (NTF). Standard NTF algorithms are very restricted in the size of tensors that can be decomposed. Our algorithm overcomes this size restriction by interpreting the tensor as a set of sub-tensors and by proceeding the decomposition of sub-tensor by sub-tensor. This approach requires only one sub-tensor at once to be available in memory.
Keywords : CANDECOMP/PARAFAC Decomposition Incremental algorithm Learning method Matrix factorization Non-negative tensor decomposition NTF
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Journal articles
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https://hal.archives-ouvertes.fr/hal-01629626
Contributor : Frédéric Davesne <>
Submitted on : Monday, November 6, 2017 - 3:42:03 PM
Last modification on : Tuesday, June 30, 2020 - 11:56:09 AM

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Vincent Vigneron, Andreas Kodewitz, Michele Nazareth da Costa, Ana Maria Tome, Elmar Langlang. Non-negative sub-tensor ensemble factorization (NsTEF) algorithm. A new incremental tensor factorization for large data sets.. Signal Processing, Elsevier, 2018, 144, pp.77--86. ⟨10.1016/j.sigpro.2017.09.012⟩. ⟨hal-01629626⟩

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