Work

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

Year: 2018

Type: conference-paper

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.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp.2018.8461790

Authors: Shunsuke Ono, Takuma Kasai

Institutions: Tokyo Institute of Technology, RIKEN

Language: English

Topic: Tensor decomposition and applications

Subfield: Computational Mathematics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Japan Society for the Promotion of Science, Precursory Research for Embryonic Science and Technology

Award: Randomized hyperspectral imaging: Foundations and applications