Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2017
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| _version_ | 1866914851061760000 |
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| author | Ono, Shunsuke Kasai, Takuma |
| author_facet | Ono, Shunsuke Kasai, Takuma |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1711_00603 |
| institution | arXiv |
| publishDate | 2017 |
| record_format | arxiv |
| spellingShingle | Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting Ono, Shunsuke Kasai, Takuma Numerical Analysis 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. |
| title | Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/1711.00603 |