Non-Linear Dimensionality Reduction Techniques of Distance-Preserving Projection for Visualization and Classification

LIU Zhong-hua; ZHOU Jing-bo; CHEN Yi; JIN Zhong

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Non-Linear Dimensionality Reduction Techniques of Distance-Preserving Projection for Visualization and Classification

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- Non-Linear Dimensionality Reduction Techniques of Distance-Preserving Projection for Visualization and Classification
- Acta Electronica Sinica Vol. 37, Issue 8, Pages: 1820-1825(2009)
- 作者机构：
  
  1. 南京理工大学计算机科学与技术学院,江苏,南京,210094
  2. 河南科技大学电子信息工程学院,河南,洛阳,471003
  3. 南京理工大学计算机科学与技术学院江苏南京,210094
  4. 河南科技大学电子信息工程学院河南洛阳,471003
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP181
- Published：2009
- 稿件说明：
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LIU Zhong-hua, ZHOU Jing-bo, CHEN Yi, et al. Non-Linear Dimensionality Reduction Techniques of Distance-Preserving Projection for Visualization and Classification[J]. Acta Electronica Sinica, 2009, 37(8): 1820-1825.
DOI：

LIU Zhong-hua, ZHOU Jing-bo, CHEN Yi, et al. Non-Linear Dimensionality Reduction Techniques of Distance-Preserving Projection for Visualization and Classification[J]. Acta Electronica Sinica, 2009, 37(8): 1820-1825. DOI：

摘要

本文对高维数据距离保持投影方法进行了改进和扩展

采用测地线距离代替欧氏距离

能够正确地展开数据所在的流形

同时又准确地保留了每个数据点到其最近邻点和部分近邻点之间的距离.为了减少邻域大小难以选取问题

采取了对邻域大小不甚敏感的P-ISOMAP算法.与原方法和ISOMAP等高维数据降维方法相比

本文方法能更好地对数据进行降维和可视化.并且

为了进行分类

本文扩展了新的分类技术.实验表明本文方法在可视化、降维和分类方面效果不错.

Abstract

This paper has extended and improved the method of distance-preserving projection which substitutes an estimated geodesic distance for the conventional Euclidean distance.It can nuroll the data of nonlinear surface correctly and preserves exact distances of each data point to its nearest neighbor point and to some other near neighbors.In order to choose a suitable neighborhood size effectively

the P-ISOMAP algorithm which is much less sensitive to the neighborhood size is used.Contrasted with the original method and ISOMAP and other methods

this paper can reduce dimensionality and visualize more effectively.For classification

this paper develops new classification techniques.The experiments prove that the method has taken the excellent effect in visualization

dimensionality reduction and classification.

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P-ISOMAP:A New ISOMAP-Based Data Visualization Algorithm with Less Sensitivity to the Neighborhood Size

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