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arXiv · 1711.00603

Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting

Shunsuke Ono·Takuma Kasai
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Abstract

Tensor factorization with hard and/or soft constraints has played an important role in signal processing and data analysis. However, existing algorithms for constrained tensor factorization have two drawbacks: (i) they require matrix-inversion; and (ii) they cannot (or at least is very difficult to) handle structured regularizations. We propose a new tensor factorization algorithm that circumvents these drawbacks. The proposed method is built upon alternating optimization, and each subproblem is solved by a primal-dual splitting algorithm, yielding an efficient and flexible algorithmic framework to constrained tensor factorization. The advantages of the proposed method over a state-of-the-art constrained tensor factorization algorithm, called AO-ADMM, are demonstrated on regularized nonnegative tensor factorization.

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Shunsuke Ono, Takuma Kasai. 2017-11-02. Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting. https://doi.org/10.1109/icassp.2018.8461790

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