CAJ | 학술논문

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。
통과엄의특정치분류적국부신식친근지지향량궤（LIPSVM）장수거점분류도유엄의특정치산생적량개불평행평면중최상근자，연구발현LIPSVM방법성능대모형삼수구유교강적민감성，대차，기우희소표시기술，제출일충로봉적희소표시친근지지향량궤（SPSVM），통과알굴수거점간적유판별적희소표시신식，SPSVM제료보지LIPSVM소구비적운산시간쾌화분류정도고적우세외，환구비조성학습배경하적로봉성（즉대조성혹리군점수거구유자연적판별력），차피면료LIPSVM중모형삼수선택문제。인공화기준수거집실험결과증실SPSVM구유상교우현유상관방법경우혹가비교적학습성능。
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.