Model Selection for Tensor Factorization and its Applications for Big Data Analysis
| Project/Area Number |
15K16067
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Soft computing
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| Research Institution | Nagoya Institute of Technology |
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Principal Investigator |
Yokota Tatsuya 名古屋工業大学, 工学(系)研究科(研究院), 助教 (80733964)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | テンソル分解 / モデル選択 / 情報量基準 / テンソル補完 / テンソル核ノルム / テンソル総変動 / テンソル因子分解 / 非負行列分解 / 凸最適化 / 主双対分離 / 核ノルム / Total Variation / PET画像再構成 / テンソル最適化 / 全変動 / 近接分離 / 多重線形ランク / 平滑制約 / 低ランク |
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| Outline of Final Research Achievements |
Tensor is a name of multi-dimensional array including vectors and matrices. For analyzing tensors, there are many approaches such as tensor factorization and tensor networks. However, model selection (i.e., rank estimation) is a critical issue of tensor factorization and its applications. In this study, we tackled the problem of model selection in tensor analysis while developing many algorithms for tensor rank estimation, noise reduction, completion, and super-resolution. Totally, we published four journal papers including arXiv and fourteens conference presentations including domestic and international conferences. It includes two highly impact journal papers published in the IEEE Transactions on Signal Processing, and two highly impact international conference papers accepted for the IEEE Conference on Computer Vision and Pattern Recognition.
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Report
(4 results)
Research Products
(15 results)
