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http://dx.doi.org/10.5302/J.ICROS.2015.14.0121

Semi-supervised Learning for the Positioning of a Smartphone-based Robot  

Yoo, Jaehyun (School of Mechanical and Aerospace Engineering, Seoul National University)
Kim, H. Jin (School of Mechanical and Aerospace Engineering, Seoul National University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.21, no.6, 2015 , pp. 565-570 More about this Journal
Abstract
Supervised machine learning has become popular in discovering context descriptions from sensor data. However, collecting a large amount of labeled training data in order to guarantee good performance requires a great deal of expense and time. For this reason, semi-supervised learning has recently been developed due to its superior performance despite using only a small number of labeled data. In the existing semi-supervised learning algorithms, unlabeled data are used to build a graph Laplacian in order to represent an intrinsic data geometry. In this paper, we represent the unlabeled data as the spatial-temporal dataset by considering smoothly moving objects over time and space. The developed algorithm is evaluated for position estimation of a smartphone-based robot. In comparison with other state-of-art semi-supervised learning, our algorithm performs more accurate location estimates.
Keywords
semi-supervised learning; wifi indoor localization; smartphone-based robot;
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Times Cited By KSCI : 3  (Citation Analysis)
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