Kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with

包含间隔偏最小二乘，组合偏最小二乘，和间隔主成分分析-Contains the interval partial least squares, combination of partial least squares, and spacing of principal component analysis

主成分分析.mht：介绍主成分分析的文章 edgepick.m：本程序的目的是实现边缘检测 drawprim.m：找到最大主分量所在的位置，从而在原图象中绘出 practical.m：一个主分量的程序，边缘检测和主分量提取 prin.m：该程序是用princomp函数来提取矩阵的主成分-Principal component analysis. Mht: the article describes the principal component analysis edgepick