As a new unsupervised learning method

Local Linear Embedding algorithm(LLE)aims at reducing the nonlinear dimensionality.Since the local linear embedding method has many disadvantages

a new method

namely robust linear embedding method based on a kernel function

is presented to solve this problem. Firstly

the kernel function is utilized to adjust the Euclidean distance between data points

so the new method can improve the performance and the range of application of LLE. Secondly

the new method using the improved W is selected because it is insensitive to noise. It is shown that the actual computation of the subspace is reduced to a standard eigenvalue problem. The proposed method was tested and evaluated in the Yale face database and AT&T face database. Nearest neighborhood (NN)algorithm was used to construct classifiers. The experimental results showed that the improved algorithm has good performance when pose

lighting condition

face expression and train sample number change.