摘要
针对在某些应用领域对二分类数据分类结果可视化的需求,以及现有无监督可视化算法无法提供分类结果的相关信息的问题,提出了二分类数据分类结果可视化算法———支持向量可视化(SVV).该算法是在无监督的自组织神经网络(SOM)的可视化功能的基础上,结合监督学习的支持向量机(SVM)的二分类算法,得到能够直观地显示高维数据、二分类数据分类边界以及数据与分类边界距离的二维映射图,提高了分类结果的可解释性.以SOM可视化算法以及Sammon算法为参照,用2组可分性不同的样本集进行仿真分析,验证了该算法的有效性和可行性.
A new algorithm called support vector visualization （SVV） was proposed for visualization of classification results of two-category data to meet the need in some applications. The SVV algorithm is based on support vector machine （SVM） and self-organizing mapping （SOM）. The result of SVV is a 2D map to visualize high-dimensional data, the boundary of the two-category data, as well as the distance between a datum and the boundary. Compared with SOM and Sammon mapping algorithms, experimental results on two datasets with different separability verify the feasibility and effectiveness of the SVV algorithm.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2006年第3期329-334,共6页
Journal of Southwest Jiaotong University
基金
国家重点基础研究发展规划项目(G1998020312)
