Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Localization for mobile sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Practical robust localization over large-scale 802.11 wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Adaptive Temporal Radio Maps for Indoor Location Estimation
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
A kernel-based learning approach to ad hoc sensor network localization
ACM Transactions on Sensor Networks (TOSN)
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
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
WiFi-SLAM using Gaussian process latent variable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Self-mapping in 802.11 location systems
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Real world activity recognition with multiple goals
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Transferring localization models over time
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Activity recognition: linking low-level sensors to high-level intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Transfer learning via multi-view principal component analysis
Journal of Computer Science and Technology - Special issue on natural language processing
Transductive relational classification in the co-training paradigm
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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Accurately locating users in a wireless environment is an important task for many pervasive computing and AI applications, such as activity recognition. In a WiFi environment, a mobile device can be localized using signals received from various transmitters, such as access points (APs). Most localization approaches build a map between the signal space and the physical location space in a offline phase, and then using the received-signal-strength (RSS) map to estimate the location in an online phase. However, the map can be outdated when the signal-strength values change with time due to environmental dynamics. It is infeasible or expensive to repeat data calibration for reconstructing the RSS map. In such a case, it is important to adapt the model learnt in one time period to another time period without too much recalibration. In this paper, we present a location-estimation approach based on Manifold co-Regularization, which is a machine learning technique for building a mapping function between data. We describe LeManCoR, a system for adapting the mapping function between the signal space and physical location space over different time periods based on Manifold Co-Regularization. We show that LeManCoR can effectively transfer the knowledge between two time periods without requiring too much new calibration effort. We illustrate LeMan-CoR's effectiveness in a real 802.11 WiFi environment.