Image feature representation by the subspace of nonlinear PCA

In subspace pattern recognition, the basis vectors represent the features of the data and define the class. In the previous works, the standard principal component analysis is used to derive the basis vectors. Compared with the standard PCA, a nonlinear PCA can provide the high-order statistics and result in non-orthogonal basis vectors. We combine a nonlinear PCA and a subspace classifier to extract the edge and line features in an image. The simulation results indicate that the basis vectors from the nonlinear PCA can classify the edge patterns better than those from a linear PCA.