A Study on Large Scale Data Analysis Method and Theoretical Evaluation based on Distance Metric Learning
| Project/Area Number |
26750118
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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 |
Social systems engineering/Safety system
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| Research Institution | Shonan Institute of Technology (2016)
Waseda University (2014-2015) |
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Principal Investigator |
Mikawa Kenta 湘南工科大学, 工学部, 講師 (40707733)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2016: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 計量距離学習 / 機械学習 / パターン認識 / 正則化 / メトリックラーニング / ベクトル空間モデル |
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| Outline of Final Research Achievements |
The development in information technology highlighted the importance of knowledge discovery from enormous electronic data. We focus on the distance metric learning which is one of the methods of machine learning. In this study, we propose the regularization methods and the way to select the suitable training data in order to reduce computational complexity of distance metric learning. In addition, we propose the way to obtain multiple distance metrics and integrate those in order to improve the classification accuracy. Consequently, we clarify the effectiveness of our proposed methods.
These methods can be used properly according to the characteristics of the analysis (e.g. to gain high performance, low computational complexity and so on). By using these method properly, it can be implemented various types of analysis.
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Report
(4 results)
Research Products
(25 results)
