关键词: 核方法, 优化问题, 缺陷检测, 核主成分分析, 图像复原

Abstract: The kernel function methods can discover the nonlinear distribution rules among the images of high precision prints. And the mining capacity is decided by the kernel function and its parameters. Selecting the kernel function is imminent to the development and application in kernel function theory. Based on the intelligent detection of prints, a new learning kernel method based on the optimization was presented for the industry of high precision printing to make the kernel function method to achieve optimal performance. Unlike the traditional calculation method, the kernel's parameter was continuously changing in kernel space, which meant that the learning scope expanded one dimension. The experimental results show that the iterative algorithm based on the theoretical analysis only needs ten iterations to get the statistical optimal kernel function and its parameters, and the recovery error of the kernel function is statistically minimum.

Key words: kernel method, optimization problem, defect detection, Kernel Principal Component Analysis (KPCA), image reconstruction