Non-Negative Tensor Factorization using Alpha and Beta Divergences
Andrzej S Cichocki, Rafał Zdunek, Seungjin Choi, Robert J. Plemmons, Шун-ичи Амари · 2007
In this paper we propose new algorithms for 3D tensor decomposition/factorization with many potential applications, especially in multi-way blind source separation (BSS), multidimensional data analysis, and sparse signal/image representations. We derive and compare three classes of algorithms: multiplicative, fixed-point alternating least squares (FPALS) and alternating interior-point gradient (AIPG) algorithms. Some of the proposed algorithms are characterized by improved robustness, efficiency and convergence rates and can be applied for various distributions of data and additive noise.