Kernels and linear discriminant analysis Suppose you wish to carry out a linear discriminant analysis (two classes) using a vector of transformations of the input variables h(x). Since h(x) is high-dimensional, you will use a regularized within-class covariance matrix Wh + γI. Show that the model can be estimated using only the inner products K(xi , xi ‘ ) = h(xi), h(xi ‘)). Hence the kernel property of support vector machines is also shared by regularized linear discriminant analysis.