Abstract: As a Generalized Eigen-value based Local Information Proximal SVM（LIPSVM）aims at assigning data points to the closer of two nonparallel planes which are generated by their corresponding generalized eigen-value problems in LIPSVM. LIPSVM owns superiorities in both computation time and test correctness. Existing researches show that the performance of LIPSVM is sensitive to the parameters of model. To address this issue in LIPSVM, following the geometric intuition of LIPSVM, a robust classification method called sparse representation Proximal Support Vector Machine（PSVM） based on sparse representation technology is proposed. By exploring the discriminative sparse representation information among the training points, SPSVM not only keeps aforementioned characteristics of LIPSVM, but also has its additional advantages, e.g., robustness to noise data or outliers and avoiding the model parameters selection problem in LIPSVM. Experimental results on the artificial and benchmark datasets demonstrate the comparable learning performance of SPSVM with respect to several exiting methods.

Key words: sparse representation, proximal classification, manifold learning, robustness

摘要： 通过广义特征值分类的局部信息亲近支持向量机（LIPSVM）将数据点分类到由广义特征值产生的两个不平行平面中最相近者，研究发现LIPSVM方法性能对模型参数具有较强的敏感性，对此，基于稀疏表示技术，提出一种鲁棒的稀疏表示亲近支持向量机（SPSVM），通过挖掘数据点间的有判别的稀疏表示信息，SPSVM除了保持LIPSVM所具备的运算时间快和分类精度高的优势外，还具备噪声学习环境下的鲁棒性（即对噪声或离群点数据具有自然的判别力），且避免了LIPSVM中模型参数选择问题。人工和基准数据集实验结果证实SPSVM具有相较于现有相关方法更优或可比较的学习性能。

关键词: 稀疏表示, 亲近分类, 流形学习, 鲁棒性