Machine Learning - Special issue on inductive transfer
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
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
Large-scale localization from wireless signal strength
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Co-localization from labeled and unlabeled data using graph Laplacian
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
WiFi-SLAM using Gaussian process latent variable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Activity recognition: linking low-level sensors to high-level intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Transfer Learning beyond Text Classification
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Three challenges in data mining
Frontiers of Computer Science in China
Cross-domain activity recognition via transfer learning
Pervasive and Mobile Computing
Distance metric learning under covariate shift
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Learning and transferring geographically weighted regression trees across time
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
| Hi-index | 0.00 |
Machine learning approaches to indoor WiFi localization involve an offline phase and an online phase. In the offline phase, data are collected from an environment to build a localization model, which will be applied to new data collected in the online phase for location estimation. However, collecting the labeled data across an entire building would be too time consuming. In this paper, we present a novel approach to transferring the learning model trained on data from one area of a building to another. We learn a mapping function between the signal space and the location space by solving an optimization problem based on manifold learning techniques. A low-dimensional manifold is shared between data collected in different areas in an environment as a bridge to propagate the knowledge across the whole environment. With the help of the transferred knowledge, we can significantly reduce the amount of labeled data which are required for building the localization model. We test the effectiveness of our proposed solution in a real indoor WiFi environment.