| 融合LLE和ISOMAP的非线性降维方法 |
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| 引用本文： | 张少龙,巩知乐,廖海斌.融合LLE和ISOMAP的非线性降维方法[J].计算机应用研究,2014,31(1):277-280. |
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| 作者姓名： | 张少龙 巩知乐 廖海斌 |
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| 作者单位： | 1. 平顶山工业职业技术学院 计算机与软件工程学院, 河南 平顶山 467000; 2. 湖北科技学院 计算机科技与技术学院, 湖北 咸宁 437100 |
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| 基金项目： | 中央高校基本科研业务费专项资金资助项目（20102120103000004）; 河南省重大科技攻关项目（072SGZS38042） |
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| 摘 要： | 局部线性嵌入（LLE）和等距映射（ISOMAP）在降维过程中都只单一地保留数据集的某一种特性结构, 从而使降维后的数据集往往存在顾此失彼的情况。针对这种情况, 借助流形学习的核框架, 提出融合LLE和ISOMAP的非线性降维方法。新的融合方法使降维后的数据集既保持着数据点间的局部邻域关系, 也保持着数据点间的全局距离关系。在仿真数据集和实际数据集上的实验结果证实了该方法的优越性。
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| 关 键 词： | 人脸识别 流形学习 数据降维 全局距离保持 局部结构保持 |
Nonlinear dimensionality reduction method by fusing LLE and ISOMAP |
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| ZHANG Shao-long,GONG Zhi-le,LIAO Hai-bin.Nonlinear dimensionality reduction method by fusing LLE and ISOMAP[J].Application Research of Computers,2014,31(1):277-280. |
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| Authors: | ZHANG Shao-long GONG Zhi-le LIAO Hai-bin |
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| Affiliation: | 1. School of Computer & Software Engineering, Pingdingshan Institute of Industry Technology, Pingdingshan Henan 467000, China; 2. School of Computer Science & Technology, Hubei University of Science Technology, Xianning Hubei 430079, China |
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| Abstract: | LLE(local linear embedding) and ISOMAP (Isometric map) only preserved one specific feature of the data sets during the dimensionality reduction process, and ignored other meaningful features. So the features of the original data sets could not be preserved as well as possible after dimensionality reduction. This paper proposed a new method that could better solve this problem. This method could preserve both the neighborhood relationships and the global pairwise distances of the high-dimensional data sets. Experiments on both artificial and real data sets prove effectiveness of the proposed method. |
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| Keywords: | face recognition manifold learning data dimensionality reduction global distances preservation local structures preservation |
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