Parallel algorithms for large scale constrained tensor decompositionParallel algorithms for large scale constrained tensor decomposition Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2015-11-172015enMost tensor decomposition algorithms were developed for in-memory computation on a single machine. There are a few recent exceptions that were designed for parallel and distributed computation, but these cannot easily incorporate practically important constraints, such as nonnegativity. A new constrained tensor factorization framework is proposed in this paper, building upon the Alternating Direction method of Multipliers (ADMoM). It is shown that this simplifies computations, bypassing the need to solve constrained optimization problems in each iteration, yielding algorithms that are naturally amenable to parallel implementation. The methodology is exemplified using nonnegativity as a baseline constraint, but the proposed framework can incorporate many other types of constraints. Numerical experiments are encouraging, indicating that ADMoM-based nonnegative tensor factorization (NTF) has high potential as an alternative to state-of-the-art approaches.http://creativecommons.org/licenses/by-nc-nd/4.0/Acoustics, Speech and Signal Processing IEEE International Conference onAcoustics, Speech and Signal Processing IEEE International Conference on Liavas Athanasios Λιαβας Αθανασιος Sidiropoulos Nikos Σιδηροπουλος Νικολαος