基于类内方差和位置敏感哈希的多经验核学习  

Multiple empirical kernel learning based on intra-class variance and locality-sensitive hashing

作　　者：黄金玻 粟兴旺 吴琳 许茹玉 王晓明 Huang Jinbo;Su Xingwang;Wu Lin;Xu Ruyu;Wang Xiaoming(School of Computer&Software Engineer,Xihua University,Chengdu 610039,China)

机构地区：[1]西华大学计算机与软件工程学院,成都610039

出　　处：《计算机应用研究》2022年第11期3340-3345,共6页Application Research of Computers

基　　金：四川省自然科学基金资助项目(2022NSFSC0533)。

摘　　要：多随机经验核学习机(multiple random empirical kernel learning machine,MREKLM)选取少量样本来构造经验特征空间,但在投影时没有考虑数据的分布信息,并且样本选择时间长。为了利用样本的分布信息,引入了类内散度矩阵,提出了基于类内方差的多经验核学习(ICVMEKL),使得样本在投影时能考虑样本类内信息,强化了分类边界,提高了分类精度。进一步,为了降低样本选择时间,利用了基于位置敏感哈希的边界点提取方法(BPLSH)来选择样本,提出了基于位置敏感哈希算法改进的ICVMEKL(ICVMEKL_I),使构建经验核的样本不再需要从候选集中获取,降低了训练时间。多个数据集上的实验结果表明,ICVMEKL能有效提高精度,ICVMEKL_I能大幅降低训练时间,两者都表现出了良好的泛化性能。MREKLM selects a small number of samples to construct the empirical feature space,but does not take into account the distribution of data when projecting,and takes a long time to select samples.In order to utilize the distribution information of samples,this paper introduced the intra-class dispersion matrix,and proposed an intra-class variance based multiple empirical kernel learning(ICVMEKL),which enabled samples to take into account the intra-class information when projecting.The method enhanced the classification boundary and improved the classification accuracy.Further,in order to reduce the selection time of samples,this paper proposed an improved ICVMEKL(ICVMEKL_I) based on locality-sensitive hashing algorithm by using the border point extraction based on locality-sensitive hashing(BPLSH) to select samples,so that the samples for constructing the empirical kernel no longer needed to be obtained from the candidate set,and the method reduced the training time.Experiments on multiple datasets show that ICVMEKL can effectively improve the accuracy,ICVMEKL_I can significantly reduce the training time,both of which show good generalization performance.

关 键 词：多核学习 经验核映射 模式识别 核方法 机器学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]