3D location-pointing as a navigation aid in Virtual Environments
Proceedings of the working conference on Advanced visual interfaces
A kernel-based learning approach to ad hoc sensor network localization
ACM Transactions on Sensor Networks (TOSN)
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Proceedings of the 10th international conference on Human computer interaction with mobile devices and services
Pedestrian localisation for indoor environments
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Adaptive Localization through Transfer Learning in Indoor Wi-Fi Environment
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
A manifold regularization approach to calibration reduction for sensor-network based tracking
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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Wi-Fi based indoor 3D localization is becoming increasingly prevalent in today's pervasive computing applications. However, traditional methods can not provide accurate predicting result with sparse training data. This paper presented an approach of indoor mobile 3D location estimation based on TRM (Transfer Regression Model). TRM can reuse well the collected data from the other floor of the building, and transfer knowledge from the large amount of dataset to the sparse dataset. TRM also import large amount of unlabeled training data which contributes to reflect the manifold feature of wireless signals and is helpful to improve the predicting accuracy. The experimental results show that by TRM, we can achieve higher accuracy with sparse training dataset compared to the regression model without knowledge transfer.