Population-guided large margin classifier for high-dimension low-sample-size problems

In this paper, we propose a novel linear binary classifier, denoted by population-guided large margin classifier (PGLMC), applicable to any sorts of data, including high-dimensional low-sample-size (HDLSS). PGLMC is conceived with a projecting direction w given by the comprehensive consideration of local structural information of the hyperplane and the statistics of the training samples. Our proposed model has several advantages compared to those widely used approaches. First, it isn't sensitive to the intercept term b. Second, it operates well with imbalanced data. Third, it is relatively simple to be implemented based on Quadratic Programming. Fourth, it is robust to the model specification for various real applications. The theoretical properties of PGLMC are proven. We conduct a series of evaluations on the simulated and five realworld benchmark data sets, including DNA classification, medical image analysis and face recognition. PGLMC outperforms the state-of-theart classification methods in most cases, or obtains comparable results.