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Open AccessArticle

Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

1
University of Carthage, Higher School of Communication of Tunis, LR-11/TIC-03 Innov’COM Laboratory, 2083 Ariana, Tunisia
2
Conservatoire National des Arts et Métiers, CEDRIC/ LAETITIA Laboratory, 75003 Paris, France
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(12), 2414; https://doi.org/10.3390/app9122414
Received: 7 April 2019 / Revised: 6 June 2019 / Accepted: 8 June 2019 / Published: 13 June 2019
(This article belongs to the Special Issue IoT for Smart Cities)
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Abstract

In this paper, we propose a high accuracy fingerprint-based localization scheme for the Internet of Things (IoT). The proposed scheme employs mathematical concepts based on sparse representation and matrix completion theories. Specifically, the proposed indoor localization scheme is formulated as a simple optimization problem which enables efficient and reliable algorithm implementations. Many approaches, like Nesterov accelerated gradient (Nesterov), Adaptative Moment Estimation (Adam), Adadelta, Root Mean Square Propagation (RMSProp) and Adaptative gradient (Adagrad), have been implemented and compared in terms of localization accuracy and complexity. Simulation results demonstrate that Adam outperforms all other algorithms in terms of localization accuracy and computational complexity. View Full-Text
Keywords: Adadelta; adaptative gradient; adaptative moment estimation; Gradient descent; indoor localization; matrix completion; Nesterov accelerated gradient; root mean square propagation; trilateration Adadelta; adaptative gradient; adaptative moment estimation; Gradient descent; indoor localization; matrix completion; Nesterov accelerated gradient; root mean square propagation; trilateration
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Njima, W.; Zayani, R.; Ahriz, I.; Terre, M.; Bouallegue, R. Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization. Appl. Sci. 2019, 9, 2414.

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