摘要
针对问题维数的增加以及不同特征对分类的作用往往不一样,导致k子凸包分类性能降低等问题,设计自适应特征选择加权k子凸包分类方法。根据传统凸包距离存在的不足引入加权k子凸包距离,在测试样本的k邻域内引入距离度量学习技术和正则化技术进行自适应的特征选择,并将自适应特征选择无缝嵌入加权k子凸包优化模型中,这样就能为不同的测试样本在不同的类别中学习自适应特征空间,得到有效的加权k子凸包距离计算方法。试验结果表明,该方法不仅能够进行降维,而且具有明显的分类性能优势。
Because of the increase of the dimension of the problem and the effect of different features on classifier,the performance of the k sub-convex-hull classifier was seriously reduced. An adaptive feature selection weighted k sub-convex-hull classifier was designed（ AWCH）. A weighted k sub-convex-hull classifier was designed according to the shortcomings of conventional convex hull distance. By applying the distance metric learning and regularization technique in the k neighborhood of the test sample,an adaptive feature selection method was designed and seamlessly integrated into the optimization model on the weighted k sub-convex-hull.Through these efforts,for different test samples,an adaptive feature space in different categories could be extracted,and a valid weighted k sub-convex-hull distance could be obtained. Experimental results showed that the AWCH not only reduced the dimension of the problem,but also was significantly superior to similar classifiers.
作者
牟廉明
MOU Lianming(College of Mathematics and Information Science,Neijiang Normal University,Neijiang 641100,Sichuan,China;Data Recovery Key Laboratory of Sichuan Province,Neijiang 641100,Sichuan,China)
出处
《山东大学学报（工学版）》
CAS
北大核心
2018年第5期32-37,共6页
Journal of Shandong University（Engineering Science）
基金
国家自然科学基金资助项目(10872085)
四川省科技厅科技计划重点资助项目(2017JY0199)
四川教育厅自然科学重点项目基金资助项目(13ZA0008)
2015内江市科技支撑计划资助项目
关键词
加权k子凸包
度量学习
正则化
特征选择
自适应
weighted k sub-convex-hull classifier
distance metric learning
regularization
feature selection
adaptive
