摘要: 提出L1范数正则化支持向量机(SVM)聚类算法。该算法能够同时实现聚类和特征选择功能。给出L1范数正则化SVM聚类原问题和对偶问题形式,采用类似迭代坐标下降的方法求解困难的混合整数规划问题。在多组数据集上的实验结果表明,L1范数正则化SVM聚类算法聚类准确率与L2范数正则化SVM聚类算法相近,而且能够实现特征选择。
关键词:
支持向量机,
L1范数,
正则化,
特征选择,
聚类,
对偶问题
Abstract: This paper proposes a L1-norm regularized Support Vector Machine(SVM) clustering algorithm. The proposed clustering algorithm can fulfill simultaneous clustering construction and feature selection. Primal and dual form of L1-norm regularized SVM clustering problem is introduced, and an algorithm that iterates coodinate-wise desent approach is adopted to solve difficult mixed integer programming. Experimental results on real datasets show that the predict accuracy of the presented algorithm is compared with L2-norm regularized SVM clustering algorithm, and can take feature selection.
Key words:
Support Vector Machine(SVM),
L1-norm,
regularization,
feature selection,
clustering,
dual problem
中图分类号:
刘建伟, 李双成, 付捷, 罗雄麟. L1范数正则化SVM聚类算法[J]. 计算机工程, 2012, 38(12): 185-187.
LIU Jian-Wei, LI Shuang-Cheng, FU Cha, LUO Xiong-Lin. L1-norm Regularized SVM Clustering Algorithm[J]. Computer Engineering, 2012, 38(12): 185-187.