An efficient nonlinear subspace learning method

kernel neighborhood preserving projections (KNPP)

is developed.The main idea is to approximate the classical local linear embedding (LLE) by introducing a linear transformation matrix and then find the solution in a very high dimensional space by kernel trick.The actual computation of the subspace is reduced to a standard eignenvalue problem rather than the generalized one.Experiments on AR face database demonstrate the effectiveness of the proposed method.