| Presentation | 2014-11-18 Learning from Positive and Unlabeled Data 2 : Computationally Efficient Estimation of Class Priors PLESSIS Marthinus Christoffel DU, Gang NIU, Masashi SUGIYAMA, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We consider the problem of estimating the class prior in an unlabeled dataset. Under the assumption that an additional labeled dataset is available, the class prior can be estimated by fitting a mixture of class-wise data distributions to the unlabeled data distribution. However, in practice, such an additional labeled dataset is often not available. In this paper, we show that, with additional samples coming only from the positive class, the class prior of the unlabeled dataset can be estimated correctly. Our key idea is to use properly penalized divergences for model fitting to cancel the error caused by the absence of negative samples. We further show that the use of the penalized L_1-distance gives a computationally efficient algorithm with an analytic solution, and establish its uniform deviation bound and estimation error bound. Finally, we experimentally demonstrate the usefulness of the proposed method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Learning from positive and unlabeled data / class-prior estimation / divergence matching |
| Paper # | IBISML2014-66 |
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| Conference Information | |
| Committee | IBISML |
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| Conference Date | 2014/11/10(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Learning from Positive and Unlabeled Data 2 : Computationally Efficient Estimation of Class Priors |
| Sub Title (in English) | |
| Keyword(1) | Learning from positive and unlabeled data |
| Keyword(2) | class-prior estimation |
| Keyword(3) | divergence matching |
| 1st Author's Name | PLESSIS Marthinus Christoffel DU |
| 1st Author's Affiliation | Department of Complexity Science and Engineering, University of Tokyo() |
| 2nd Author's Name | Gang NIU |
| 2nd Author's Affiliation | Baidu Inc. |
| 3rd Author's Name | Masashi SUGIYAMA |
| 3rd Author's Affiliation | Department of Complexity Science and Engineering, University of Tokyo |
| Date | 2014-11-18 |
| Paper # | IBISML2014-66 |
| Volume (vol) | vol.114 |
| Number (no) | 306 |
| Page | pp.pp.- |
| #Pages | 7 |
| Date of Issue | |