Similarity Measures for Nearest Neighbor Search and Classification Methods in High Dimensional and Large Number of Data
Project/Area Number |
25730142
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Research Category |
Grant-in-Aid for Young Scientists (B)
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Allocation Type | Multi-year Fund |
Research Field |
Intelligent informatics
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Research Institution | Yamagata University (2015-2016) National Institute of Genetics (2013-2014) |
Principal Investigator |
Suzuki Ikumi 山形大学, 大学院理工学研究科, 助教 (20637730)
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Project Period (FY) |
2013-04-01 – 2016-03-31
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Project Status |
Completed (Fiscal Year 2016)
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Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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Keywords | ハブネス / ハブの軽減 / センタリング / 近傍法 / カーネル法 / 協調フィルタリング / 空間中心性の消去 / ハブネスの軽減 / ローカライズドセンタリング / データ中心化 / 高次元データ / k近傍法 |
Outline of Final Research Achievements |
Recently, hubness, a phenomenon occurring in high-dimensional datasets as a result of curse of dimensionality has attracted the attention of researchers in the artificial intelligence community, especially for data mining and machine learning. In this work, we pointed out that the hubness influences the performance of k-nearest neighbor (k-NN) methods. We reported that subtracting mean vector from each sample (centering) is a simple, yet very effective for improving k-NN classification. Also, we proved that centering is effective for k-NNs, because centering reduces hubs in a dataset.
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Report
(4 results)
Research Products
(9 results)
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[Presentation] Reducing Hubness for Kernel Regression2015
Author(s)
Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu and Milos Radovanovic
Organizer
In proceedings of the 8th International Conference on Similarity Search and Applications (SISAP)
Place of Presentation
University of Strathclyde(Glasgow, UK)
Year and Date
2015-10-12
Related Report
Int'l Joint Research
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