Local Linear Dimensionality Reduction Algorithm Based on Nonlinear Manifolds Decomposition

Abstract

Aiming at the problem that linear data reduction algorithm is difficult to deal with data with nonlinear structure, this paper proposes a new algorithm for facial expression feature extraction based on manifold decomposition algorithm. The algorithm utilizes the characteristic of local linearity of nonlinear manifolds. Through classical principal component analysis The local linear patches of nonlinear manifold structures are reduced in dimension. The local PCA representation can be obtained by local dimension reduction, and then the local coordinates are aligned by the coordinate arrangement technique, so that the low dimensional coordinates of the whole manifold can be obtained. The simulation results show that the local linear dimensionality reduction algorithm of nonlinear manifold decomposition is superior to other classical manifold learning algorithms in the recognition accuracy when applied to facial expression feature extraction.