A new stochastic optimization algorithm to decompose large nonnegative tensors
Résumé
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.
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