A Temperature Data Denoising Method Using Laplacian Matrix and Neumann Series
Chien‐Cheng Tseng, Su‐Ling Lee · 2020
In this paper, a temperature data denoising method using Laplacian matrix and Neumann series is presented. First, the denoising problem is formulated as an optimization problem whose solution needs to solve the matrix inversion. Then, to avoid solving matrix inversion, the Neumann series is used to approximate the matrix inversion by a truncated series expansion. Next, two efficient distributed implementation methods are presented to realize the proposed denoising algorithm. Finally, the real temperature data in Taiwan is used to demonstrate the effectiveness of the proposed method.