Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

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.

Keywords

• Adadelta
• adaptative gradient
• adaptative moment estimation
• Gradient descent
• indoor localization
• matrix completion
• Nesterov accelerated gradient
• root mean square propagation
• trilateration