Practical and Effective Data Mining Via Local Intrinsic Dimensional Modeling
| Project/Area Number |
15H02753
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | National Institute of Informatics |
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Principal Investigator |
Houle Michael E. 国立情報学研究所, 大学共同利用機関等の部局等, 客員教授 (90399270)
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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 |
¥16,120,000 (Direct Cost: ¥12,400,000、Indirect Cost: ¥3,720,000)
Fiscal Year 2017: ¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
Fiscal Year 2016: ¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
Fiscal Year 2015: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
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| Keywords | 高次元空間 / 極値理論 / データマイニング |
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| Outline of Final Research Achievements |
In the era of Big Data, data volumes have become so enormous and so complex as to preclude processing using traditional applications. For similarity search and retrieval, as well as many other fundamental operations in such areas as data mining, machine learning, multimedia, recommendation systems, and bioinformatics, the efficiency and effectiveness of software implementations depends crucially on the interplay between measures of data similarity and the features (or attributes) by which data objects are represented. When the number of features (the data dimensionality) is high, the errors introduced into similarity measurements by the many irrelevant feature attributes can overwhelm the contributions of the relevant features. The overall goal of this project is to tackle the problem of the curse of dimensionality in similarity applications for big data, by developing practical unsupervised techniques that recognize and take advantage of local variations in intrinsic dimensionality.
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Report
(4 results)
Research Products
(30 results)
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[Journal Article] Estimating Local Intrinsic Dimensionality2015
Author(s)
Laurent Amsaleg, Oussama Chelly, Teddy Furon, Stephane Girard, Michael E. Houle, Ken-ichi Kawarabayashi, Michael Nett
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Journal Title
21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015)
Volume: 21
Pages: 29-38
DOI
Related Report
Peer Reviewed / Int'l Joint Research
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