PCA与Linear Regression的区别

左边为线性回归的原理 右边为PCA

Linear Regression	PCA
taking all the values of x and try to use that to predict y.	there is no special variable y that we're trying to predict
	have a list of features, x1, x2,..., xn, and all of these features are treated equally, so no one of them is special.
drawing these blue lines vertically	tries to minimize the magnitude of these blue lines, which are drawn at an angle. the shortest orthogonal distances. The shortest distance between the point x and this red line.