Proceedings

Co-Localization from Labeled and Unlabeled Data Using Graph Laplacian

Jeffrey Junfeng Pan, Qiang Yang

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Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2166-2170.

Abstract

This paper addresses the problem of recovering the locations of both mobile devices and access points from radio signals, a problem which we call co-localization, by exploiting both labeled and unlabeled data from mobile devices and access points. We first propose a solution using Latent Semantic Indexing to construct the relative locations of the mobile devices and access points when their absolute locations are unknown. We then propose a semi-supervised learning algorithm based on manifold to obtain the absolute locations of the devices. Both solutions are finally combined together in terms of graph Laplacian. Extensive experiments are conducted in wireless local-area networks, wireless sensor networks and radio frequency identification networks. The experimental results show that we can achieve high accuracy with much less calibration effort as compared to several previous systems.

Jeffrey Junfeng Pan, and Qiang Yang. 2007. Co-Localization from Labeled and Unlabeled Data Using Graph Laplacian. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07. IJCAI, 2166–2170. https://ijcai.org/proceedings/2007/349
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