Uncorrelated Discriminant Projection Based on Maximum Margin Criterion and Its Kernelized Extension

Abstract

Abstract A new linear dimensionality reduction algorithm called uncorrelated discriminant projection (UDP) is proposed in this paper. The proposed UDP algorithm is based on the maximum margin criterion (MMC) which aim at maximizing class separation after dimension reduction. By imposing an uncorrelated constraint in the objective function, UDP extracts statistically uncorrelated features which are important in many pattern recognition problems. Moreover, we propose performing UDP in reproducing kernel Hilbert space (RKHS) which leads to a nonlinear variant of UDP called kernel uncorrelated discriminant projections (KUDP). In order to demonstrate the effectiveness and efficiency of the newly proposed algorithms, we conducted experiments on two benchmark face databases. The experimental results indicates that both UDP and KUDP are able to find face subspaces optimal for recognition.