坐标下降l_2范数LS-SVM分类算法

Classification Algorithm of l_2-norm LS-SVM via Coordinate Descent

研究l2范数正则化最小二乘支持向量机的坐标下降算法实现.在图像处理、人类基因组分析、信息检索、数据管理和数据挖掘中经常会遇到机器学习目标函数要处理的数据无法在内存中处理的场景.最近研究表明大规模线性支持向量机使用坐标下降方法具有较好的分类性能,在此工作基础上,文中扩展坐标下降方法到最小二乘支持向量机上,提出坐标下降l2范数LS-SVM分类算法.该算法把LS-SVM目标函数中模型向量的优化问题简化为特征分量的单目标逐次优化问题.在高维小样本数据集、中等规模数据集和大样本数据集上的实验验证了该算法的有效性,与LS-SVM分类算法相比,在数据内存中无法处理的情况下可作为备用方法.

The coordinate descent approach for l2 norm regulated least square support vector machine is studied. The datasets involved in the objective function for machine learning have larger data scale than the memory size has in image processing, human genome analysis, information retrieval, data management, and data mining. Recently, the coordinate descent method for large-scale linear SVM has good classification performance on large scale datasets. In this paper, the results of the work are extended to the least square support vector machine, and the least square support vector machine is proposed. function is reduced to single variable optimization by high-dimension small-sample datasets, middle-scale coordinate descent approach for l2 norm regulated The vector optimization of the LS-SVM objective the proposed algorithm. The experimental results on datasets and large-scale datasets demonstrate its effectiveness. Compared to the state-of-the-art LS-SVM classifiers, the proposed method can be a good candidate when data cannot fit in memory.