【摘要】 在进行线性投影降维时,由于传统的最大间距准则(Maximum Margin Criterion,MMC)算法基于L2-范数,易于受到野值(outliers)及噪声的影响.该文提出一种基于L1-范数的最大间距准则(L1-norm-based MMC,MMCL1)降维方法,它充分利用L1-范数对野值及噪声的强鲁棒性以及最大间距准则,提出了一种快速迭代优化算法,并给出了其单调收敛到局部最优的证明.在多个图像数据库上的实验验证了该方法的鲁棒性与高效性.更多还原

【Abstract】 When performing dimensionality reduction with linear projections,maximum margin criterion( MMC) is often affected by outliers and noises due to L2-norm. In this paper,L1-norm-based maximum margin criterion( MMC-L1)is proposed for dimensionality reduction. It makes full use of Maximum Margin Criterion and strong robustness of L1-norm to outliers and noises. A rapid iterative optimization algorithm,with its proof of monotonic convergence to local optimum,is given. Experiments on several public image databases verify the robustness and efficiency of the proposed method.更多还原