Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting
書誌事項
- 公開日
- 2018-04
- 資源種別
- journal article
- DOI
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- 10.1109/icassp.2018.8461790
- 10.48550/arxiv.1711.00603
- 公開者
- IEEE
説明
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.
5 pages, submitted to ICASSP2018
収録刊行物
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- 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 3379-3383, 2018-04
IEEE
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詳細情報 詳細情報について
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- CRID
- 1360004235222933504
-
- 資料種別
- journal article
-
- データソース種別
-
- Crossref
- KAKEN
- OpenAIRE
