Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
The cricket compass for context-aware mobile applications
Proceedings of the 7th annual international conference on Mobile computing and networking
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
LANDMARC: Indoor Location Sensing Using Active RFID
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Localization for mobile sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
A kernel-based learning approach to ad hoc sensor network localization
ACM Transactions on Sensor Networks (TOSN)
MoteTrack: a robust, decentralized approach to RF-based location tracking
Personal and Ubiquitous Computing
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A distributed approach to passive localization for sensor networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Discriminatively regularized least-squares classification
Pattern Recognition
Online co-localization in indoor wireless networks by dimension reduction
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Adaptive localization in a dynamic WiFi environment through multi-view learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Co-localization from labeled and unlabeled data using graph Laplacian
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
Semi-supervised learning for WLAN positioning
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Leave-one-out manifold regularization
Expert Systems with Applications: An International Journal
Leave-One-Out cross-validation based model selection for manifold regularization
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Transfer regression model for indoor 3d location estimation
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Manifold-based canonical correlation analysis for wireless sensor network localization
Wireless Communications & Mobile Computing
Total variation regularization for training of indoor location fingerprints
Proceedings of the 2nd ACM annual international workshop on Mission-oriented wireless sensor networking
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The ability to accurately detect the location of a mobile node in a sensor network is important for many artificial intelligence (AI) tasks that range from robotics to context-aware computing. Many previous approaches to the location-estimation problem assume the availability of calibrated data. However, to obtain such data requires great effort. In this paper, we present a manifold regularization approach known as LeMan to calibration-effort reduction for tracking a mobile node in a wireless sensor network. We compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use Crossbow MICA2 to setup the network and USB camera array to obtain the ground truth. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared to several previous systems.