Combining Linear Dimensionality Reduction and Locality Preserving Projections with Feature Selection for Recognition Tasks

Recently, a graph-based method was proposed for Linear Dimensionality Reduction (LDR). It is based on Locality Preserving Projections (LPP). It has been successfully applied in many practical problems such as face recognition. In order to solve the Small Size Problem that usually affects face recognition, LPP is preceded by a Principal Component Analysis (PCA). This paper has two main contributions. First, we propose a recognition scheme based on the concatenation of the features provided by PCA and LPP. We show that this concatenation can improve the recognition performance. Second, we propose a feasible approach to the problem of selecting the best features in this mapped space. We have tested our proposed framework on several public benchmark data sets. Experiments on ORL, UMIST, PF01, and YALE Face Databases and MNIST Handwritten Digit Database show significant performance improvements in recognition.