关键词: 流形学习, 局部保持投影, 奇异值分解, 人脸识别, 模式识别

Abstract: Locality Preserving Projection (LPP) is a manifold learning method, while the face recognition application of LPP is known to suffer from singular value problem, so a solution scheme using Singular Value Decomposition (SVD) was proposed for recognition application. In this model, the sample data were projected on a non-singular orthogonal matrix to solve the problem of singular value. Then the data of the low dimensional sample space projection subspace were obtained according to the LPP method. The training samples and testing samples were projected onto low-dimensional subspace respectively. Finally the nearest neighbor classifier was used for classification. A series of experiments to compare the proposed algorithm with the traditional local projection algorithm and Principal Component Analysis (PCA) were given on ORL face database. The experimental results demonstrate the efficacy of the improved LPP approach for face recognition.

Key words: manifold learning, Locality Preserving Projection (LPP), Singular Value Decomposition (SVD), face recognition, pattern recognition