Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

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...

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Bibliographic Details
Main Authors: Wafa Njima, Rafik Zayani, Iness Ahriz, Michel Terre, Ridha Bouallegue
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Applied Sciences
Subjects:
Adadelta
adaptative gradient
adaptative moment estimation
Gradient descent
indoor localization
matrix completion
Nesterov accelerated gradient
root mean square propagation
trilateration
Online Access:https://www.mdpi.com/2076-3417/9/12/2414

Internet

https://www.mdpi.com/2076-3417/9/12/2414

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