Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning
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.
indoor location estimation wireless local area network hybrid semi-supervised learning naive bayes expectation maximization fisher kernel least square support vector machine