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，MMC-L1）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。%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.
展开▼
