Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning
Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning
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DOI：
10.1109/tmc.2011.193
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发表时间：
2012-11-01
影响因子：
7.9
通讯作者：
Chiang, Mung
中科院分区：
文献类型：
--
作者：
Ouyang, Robin Wentao;Wong, Albert Kai-Sun;Chiang, Mung
对于基于无线局域网指纹的室内位置估计，如何在保持高位置估计精度的同时减少离线校准工作是主要关注的问题。本文提出了一种混合生成/判别半监督学习算法，利用大量的未标记的样本，以补充少量的标记样本。这种混合方法使我们能够将生成模型的建模能力和灵活性与判别方法的上级性能结合起来。其他相关的问题，如学习效率的提高和分布估计平滑，也进行了讨论。大量的实验结果表明，我们提出的方法可以有效地减少校准工作，并表现出上级性能的定位精度和鲁棒性。
For indoor location estimation based on wireless local area networks fingerprinting, how to reduce the offline calibration effort while maintaining high location estimation accuracy is of major concern. In this paper, a hybrid generative/discriminative semisupervised learning algorithm is proposed that utilizes a large number of unlabeled samples to supplement a small number of labeled samples. This hybrid method allows us to combine the modeling power and flexibility of generative models with the superior performance of discriminative approaches. Other related issues, such as learning efficiency enhancement and distribution estimation smoothing, are also discussed. Extensive experimental results show that our proposed method can effectively reduce the calibration effort and exhibit superior performance in terms of localization accuracy and robustness.