Estimating the factor structure in multiple matrices
Project/Area Number |
16H02868
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Research Category |
Grant-in-Aid for Scientific Research (B)
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Allocation Type | Single-year Grants |
Section | 一般 |
Research Field |
Intelligent informatics
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Research Institution | Kyoto University |
Principal Investigator |
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Project Period (FY) |
2016-04-01 – 2019-03-31
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Project Status |
Completed (Fiscal Year 2019)
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Budget Amount *help |
¥16,120,000 (Direct Cost: ¥12,400,000、Indirect Cost: ¥3,720,000)
Fiscal Year 2018: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
Fiscal Year 2017: ¥5,850,000 (Direct Cost: ¥4,500,000、Indirect Cost: ¥1,350,000)
Fiscal Year 2016: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
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Keywords | 機械学習 / 行列分解 / 協調行列分解 / データマイニング / 知識発見とデータマイニング |
Outline of Final Research Achievements |
The objective of research is to present a generalized framework for the input of multiple matrices sharing dimensions and efficient solutions under this framework. We show two example results among our various results obtained during our research period: 1. For the input of a tensor and a matrix which share one dimension, we define a new norm and propose an efficient learning algorithm to estimate the norm. We analyze the property of the norm and empirically show the performance advantage of our norm and algorithm using both synthetic and real-world datasets. The results were summarized into a publication appeared in Neural Computation and also a paper appeared in NeurIPS, one of the top machine learning conferences. 2. We develop an efficient, scalable probabilistic-model based approach for the input of multiple matrices sharing dimensions. This result was published as a paper appeared in AAAI, one of the top artificial intelligence conferences.
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Academic Significance and Societal Importance of the Research Achievements |
次元を共有する複数の行列は、例えば、EC (electronic commerce) サイトの顧客(ユーザ)と商品(アイテム)のデータや、患者に対する薬の投与データで見受けられる。一般的な設定である。これらのデータに対する効率的・スケーラブルな機械学習手法は、将来的に多くの分野で利用される可能性があり、意義が大きいと考えられる。
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Report
(4 results)
Research Products
(40 results)
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[Journal Article] Factor Analysis on a Graph.2018
Author(s)
Karasuyama, M. and Mamitsuka, H.
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Journal Title
Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018)
Volume: -
Pages: 1117-1126
Related Report
Peer Reviewed / Open Access
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[Journal Article] Factor Analysis on a Graph2018
Author(s)
Karasuyama, M. and Mamitsuka, H.
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Journal Title
Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018) (JMLR Workshop and Conference Proceedings (PMLR))
Volume: 84
Pages: 1117-1126
Related Report
Peer Reviewed / Open Access
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[Presentation] Factor Analysis on a Graph.2018
Author(s)
Karasuyama, M. and Mamitsuka, H.
Organizer
Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018)
Related Report
Int'l Joint Research
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