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题名:
一种改进的ISOMAP分类算法
其他题名: AN IMPROVED ISOMAP CLASSIFICATION ALGORITHM
作者: 杨秀锋; 彭慧; 周晓锋
作者部门: 数字工厂研究室
关键词: 流形学习 ; 数据降维 ; 等距特征映射 ; 分类 ; 监督学习
刊名: 计算机应用与软件
ISSN号: 1000-386X
出版日期: 2015
卷号: 32, 期号:8, 页码:43-46, 55
收录类别: CSCD
产权排序: 1
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。
英文摘要: 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.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/17130
Appears in Collections:数字工厂研究室_期刊论文

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Recommended Citation:
杨秀锋,彭慧,周晓锋. 一种改进的ISOMAP分类算法[J]. 计算机应用与软件,2015,32(8):43-46, 55.
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