Migratory Logistic Regression for Learning Concept Drift Between Two Data Sets With Application to UXO Sensing
Migratory Logistic Regression for Learning Concept Drift Between Two Data Sets With Application to UXO Sensing
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DOI：
10.1109/tgrs.2008.2005268
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发表时间：
2009-05-01
影响因子：
8.2
通讯作者：
Carin, Lawrence
中科院分区：
文献类型：
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作者：
Liao, Xuejun;Carin, Lawrence
为了在监督学习中实现良好的泛化，通常需要从相同的源分布中提取训练和测试示例。在本文中，我们提出了一种在逻辑回归背景下放宽这一要求的方法。假设D-p和D-a是从两个不同的分布T和A（称为概念，借用心理学术语）抽取的两组示例，其中D-a是完全标记的，D-p是部分标记的，我们的目标是完成D-p的标签。我们为 D-a 中的每个示例引入一个辅助变量 mu 来反映其与 D-p 的不匹配。在适当的约束下，μ 与分类器一起被估计为副产品。我们还提出了一种主动学习方法来选择 D-p 中的标记示例。所提出的算法称为迁移逻辑回归，已在模拟数据以及未爆炸弹药清理感兴趣的实际测量数据上成功得到验证。
To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. In this paper, we propose a method to relax this requirement in the context of logistic regression. Assuming D-p and D-a are two sets of examples drawn from two different distributions T and A (called concepts, borrowing a term from psychology), where D-a are fully labeled and D-p partially labeled, our objective is to complete the labels of D-p. We introduce an auxiliary variable mu for each example in D-a to reflect its mismatch with D-p. Under an appropriate constraint the mu s are estimated as a byproduct, along with the classifier. We also present an active learning approach for selecting the labeled examples in D-p. The proposed algorithm, called migratory logistic regression, is demonstrated successfully on simulated data as well as on real measured data of interest for unexploded ordnance cleanup.