Parameter free large margin nearest neighbor for distance metric learning | |
Song, Kun1; Nie, Feiping2; Han, Junwei1; Li, Xuelong3; Nie, Feiping (feipingnie@gmail.com) | |
2017 | |
会议名称 | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
会议录名称 | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
页码 | 2555-2561 |
会议日期 | 2017-02-04 |
会议地点 | San Francisco, CA, United states |
出版者 | AAAI press |
产权排序 | 3 |
摘要 | We introduce a novel supervised metric learning algorithm named parameter free large margin nearest neighbor (PFLMNN) which can be seen as an improvement of the classical large margin nearest neighbor (LMNN) algorithm. The contributions of our work consist of two aspects. First, our method discards the cost term which shrinks the distances between inquiry input and its k target neighbors (the k nearest neighbors with same labels as inquiry input) in LMNN, and only focuses on improving the action to push the imposters (the samples with different labels form the inquiry input) apart out of the neighborhood of inquiry. As a result, our method does not have the parameter needed to tune on the validating set, which makes it more convenient to use. Second, by leveraging the geometry information of the imposters, we construct a novel cost function to penalize the small distances between each inquiry and its imposters. Different from LMNN considering every imposter located in the neighborhood of each inquiry, our method only takes care of the nearest imposters. Because when the nearest imposter is pushed out of the neighborhood of its inquiry, other imposters would be all out. In this way, the constraints in our model are much less than that of LMNN, which makes our method much easier to find the optimal distance metric. Consequently, our method not only learns a better distance metric than LMNN, but also runs faster than LMNN. Extensive experiments on different data sets with various sizes and difficulties are conducted, and the results have shown that, compared with LMNN, PFLMNN achieves better classification results. Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
作者部门 | 光学影像学习与分析中心 |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/29402 |
专题 | 光谱成像技术研究室 |
通讯作者 | Nie, Feiping (feipingnie@gmail.com) |
作者单位 | 1.School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi; 710072, China 2.School of Computer Science, Center for OPTIMAL, Northwestern Polytechnical University, Xi'an; 710072, China 3.Center for OPTIMAL, State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China |
推荐引用方式 GB/T 7714 | Song, Kun,Nie, Feiping,Han, Junwei,et al. Parameter free large margin nearest neighbor for distance metric learning[C]:AAAI press,2017:2555-2561. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Parameter free large(769KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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