The nature of statistical learning theory
The nature of statistical learning theory
WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Power-Efficient Access-Point Selection for Indoor Location Estimation
IEEE Transactions on Knowledge and Data Engineering
Wireless symbolic positioning using support vector machines
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Location Estimation via Support Vector Regression
IEEE Transactions on Mobile Computing
Reducing the Calibration Effort for Probabilistic Indoor Location Estimation
IEEE Transactions on Mobile Computing
Kernel-Based Positioning in Wireless Local Area Networks
IEEE Transactions on Mobile Computing
Location Fingerprinting In A Decorrelated Space
IEEE Transactions on Knowledge and Data Engineering
Adaptive spherical Gaussian kernel in sparse Bayesian learning framework for nonlinear regression
Expert Systems with Applications: An International Journal
Location determination of mobile devices for an indoor WLAN application using a neural network
Knowledge and Information Systems
Statistical learning theory for location fingerprinting in wireless LANs
Computer Networks: The International Journal of Computer and Telecommunications Networking
Intelligent Dynamic Radio Tracking in Indoor Wireless Local Area Networks
IEEE Transactions on Mobile Computing
A dynamic system approach for radio location fingerprinting in wireless local area networks
IEEE Transactions on Communications
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
LifeMap: A Smartphone-Based Context Provider for Location-Based Services
IEEE Pervasive Computing
A survey of indoor positioning systems for wireless personal networks
IEEE Communications Surveys & Tutorials
A Novel Algorithm for Multipath Fingerprinting in Indoor WLAN Environments
IEEE Transactions on Wireless Communications
On Feature Extraction via Kernels
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust indoor positioning using differential wi-fi access points
IEEE Transactions on Consumer Electronics
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Smartphone-Based Collaborative and Autonomous Radio Fingerprinting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The essential challenge in wireless local area network (WLAN) positioning system is the highly uncertainty and nonlinearity of received signal strength (RSS). These properties degrade the positioning accuracy drastically, as well as increasing the data collection cost. To address this challenge, we propose the nonlinear discriminative feature extraction of RSS using kernel direct discriminant analysis (KDDA). KDDA extracts location features in a kernel space, where the nonlinear RSS patterns are well characterized and captured. By performing KDDA, the discriminative information contained in RSS is reorganized and maximally extracted, while redundant features or noise are discarded adaptively. Furthermore, unlike previous monolithic models, we employ a location clustering step to localize the feature extraction. This step effectively avoids the suboptimality caused by variability of RSS over physical space. After feature extraction in each subregion, the relationship between extracted features and physical locations is established by support vector regression (SVR). Experimental results show that the proposed approach obtains higher accuracy while reducing the data collection cost significantly.