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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.
Cichocki et al. (Sun,) studied this question.
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