Discriminant Locality Preserving Projections Based on Neighborhood Maximum Margin

Abstract

This paper took a research about the small size sample problem of the discriminant locality preserving projections method, and proposed the discriminant locality preserving projections method based on neighborhood maximum margin (NMMDLPP). Firstly, the training sample structured a weighted of K-nearest neighbor graph, and gave the weight parameter to each side of the nearest neighbor graph for obtaining the intraclass neighbors and interclass neighbors local geometry information of each point; then reduce the interval between the intraclass neighbors and increase the interval between the interclass neighbors with the result of transfer matrix, and applied the neighbor point optimal refactoring coefficient of the data to the objective function. This method chose the difference between the locality preserving between-class scatter and the locality preserving within-class scatter as the objective function to avoid of calculating the inversion of matrix. This method has conducted an experiment on the UMIST face database and Yale face database. Experimental results show that the NMMDLPP algorithm is superior to other algorithms in recognition rate. The recognition rate can reach more than 91.4%.