一种改进的ISOMAP分类算法  

AN IMPROVED ISOMAP CLASSIFICATION ALGORITHM

作　　者：杨秀锋[1,2] 彭慧[1] 周晓锋[1] 

机构地区：[1]中国科学院沈阳自动化研究所,辽宁沈阳110016 [2]中国科学院大学,北京100084

出　　处：《计算机应用与软件》2015年第8期43-46,55,共5页Computer Applications and Software

摘　　要：传统的等距特征映射算法在降维时未考虑数据的类别标签,降维后不能够产生从高维到低维的映射矩阵,且不适用于多个类簇的情况,不能直接用于分类。针对这几个问题利用近邻元分析方法取代多维尺度分析法,并且引入特征向量作为输入矩阵,提出一种以分类为目的的等距特征映射算法(NC-ISOMAP)。降维时获取理想的低维投影矩阵,使降维后类间数据更加分开,类内数据更加紧凑。实验结果表明NC-ISOMAP算法能够取得很好的降维效果和分类性能,并在不同的数据集中有着较好的鲁棒性。Traditional isometric feature mapping algorithm does not consider the classification labels of data when reducing the dimensionality,and cannot produce a mapping matrix ranging from high dimensions to lower dimensions after the dimensionality being reduced,and it cannot fit the situation of multi-class clusters as well,so it cannot be directly used for classification. In light of these problems,we use neighbourhood component analysis（ NCA） to replace the multidimensional scaling analysis（ MDS）,introduce eigenvector as the input matrix,and propose an isometric feature mapping algorithm aiming at classification,called NC-ISOMAP. In the process of dimensionality reduction,NC-ISOMAP can obtain an ideal low dimensional project matrix,which makes the data become more separate between classes and more compact within a class after lowering the dimensionality. Experimental results show that NC-ISOMAP is able to achieve quite good dimensionality reduction result and classification performance,and has a better robustness in different datasets.

关 键 词：流形学习 数据降维 等距特征映射 分类 监督学习 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]