Kernel Neighborhood Preserving Projections for Face Recognition
Zhengkai Liu · Dianzi xuebao · 2006
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.