Title
Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning
Abstract
For indoor location estimation based on wireless local area networks fingerprinting, how to reduce the offline calibration effort while maintaining high location estimation accuracy is of major concern. In this paper, a hybrid generative/discriminative semi-supervised learning algorithm is proposed that utilizes a large number of unlabeled samples to supplement a small number of labeled samples. This hybrid method allows us to combine the modeling power and flexibility of generative models with the superior performance of discriminative approaches. Other related issues, such as learning efficiency enhancement and distribution estimation smoothing, are also discussed. Extensive experimental results show that our proposed method can effectively reduce the calibration effort and exhibit superior performance in terms of localization accuracy and robustness.
Year
DOI
Venue
2012
10.1109/TMC.2011.193
Mobile Computing, IEEE Transactions
Keywords
Field
DocType
high location estimation accuracy,discriminative learning,indoor location estimation,calibration effort,hybrid method,hybrid generative,large number,reduced calibration exploiting unlabeled,distribution estimation smoothing,generative model,superior performance,discriminative approach,probabilistic logic,indexes,accuracy,learning artificial intelligence,calibration,kernel,wireless local area network,estimation theory,estimation,data models,expectation maximization,fisher kernel,naive bayes
Data modeling,Naive Bayes classifier,Pattern recognition,Computer science,Robustness (computer science),Smoothing,Artificial intelligence,Probabilistic logic,Estimation theory,Discriminative model,Fisher kernel,Machine learning
Journal
Volume
Issue
ISSN
11
11
1536-1233
Citations 
PageRank 
References 
27
1.04
18
Authors
4
Name
Order
Citations
PageRank
Wentao Robin Ouyang11459.48
Albert K. Wong220027.16
Chin-Tau A. Lea358272.45
Mung Chiang47303486.32