论文标题
至少正方形支持各向异性扩散过滤的矢量回归
A least squares support vector regression for anisotropic diffusion filtering
论文作者
论文摘要
作为低通滤波器作为信号平滑的各向异性扩散滤波具有边缘性能的优点，即，它不会影响包含比信号其他部分更关键数据的边缘。在本文中，我们通过使用Legendre Orthoconal内核来介绍基于最小二乘支持矢量回归的数值算法，并通过曲柄 - 尼科尔森方法在时间上离散地对非线性扩散问题进行离散化。此方法将信号平滑过程转换为解决优化问题，该问题可以通过有效的数值算法来解决。在最终分析中，我们报告了一些数值实验，以显示基于机器学习的信号平滑方法的有效性。
Anisotropic diffusion filtering for signal smoothing as a low-pass filter has the advantage of the edge-preserving, i.e., it does not affect the edges that contain more critical data than the other parts of the signal. In this paper, we present a numerical algorithm based on least squares support vector regression by using Legendre orthogonal kernel with the discretization of the nonlinear diffusion problem in time by the Crank-Nicolson method. This method transforms the signal smoothing process into solving an optimization problem that can be solved by efficient numerical algorithms. In the final analysis, we have reported some numerical experiments to show the effectiveness of the proposed machine learning based approach for signal smoothing.
