2014 Fiscal Year Final Research Report
Empirical Bayes Kernels: Unsupervised Kernel Learning
Project/Area Number |
25540100
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Research Category |
Grant-in-Aid for Challenging Exploratory Research
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Allocation Type | Multi-year Fund |
Research Field |
Intelligent informatics
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Research Institution | Kyoto University |
Principal Investigator |
CUTURI Marco 京都大学, 情報学研究科, 准教授 (80597344)
|
Project Period (FY) |
2013-04-01 – 2015-03-31
|
Keywords | 機械学習 / 距離学習 / ヒストグラムデータ |
Outline of Final Research Achievements |
Our goal in this work was to provide a principled approach to carry out kernel/metric learning in an unsupervised way, to take advantage of large datasets of unlabeled data. We investigated this research avenue by focusing mostly on histogram data (bags-of-features). Using a combination of 3 known approaches by Aitchison, Lebanon and Hinton, we were able to propose different algorithms which perform at state-of-the art level or directly outperform competing approaches.
|
Free Research Field |
機械学習
|