A Local Structure Preserving Based Data Dimensionality Reduction Approach

ZHANG Lin

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A Local Structure Preserving Based Data Dimensionality Reduction Approach

更新时间：2026-09-04

- A Local Structure Preserving Based Data Dimensionality Reduction Approach
- Vol. 33, Issue 5, Pages: 76-79(2016)
- 作者机构：
  
  遵义师范学院机构计算机与信息科学学院,贵州,遵义,563002
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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张琳. 一种基于局部结构保持的数据降维方法[J]. 微电子学与计算机, 2016, 33(5): 76-79.

ZHANG Lin. A Local Structure Preserving Based Data Dimensionality Reduction Approach[J]. 2016, 33(5): 76-79.
张琳. 一种基于局部结构保持的数据降维方法[J]. 微电子学与计算机, 2016, 33(5): 76-79. DOI：

ZHANG Lin. A Local Structure Preserving Based Data Dimensionality Reduction Approach[J]. 2016, 33(5): 76-79. DOI：

摘要

针对传统线性回归方法进行数据降维

不能很好地利用数据间几何结构的问题

提出了一种基于局部结构保持的数据降维方法.利用非负约束下的最小二乘构造关系图

从而描述局部近邻几何信息.在将全局结构信息与局部结构信息整合时

设计了一种新的模型选择方法用于模型参数估计

极大地减少了计算量.在公测库ORL、YaleB、USPS、20Newgroup、Isolet上的实验结果表明

该方法用于数据分类时优于ULD、OLDA和NPE等方法.

Abstract

The traditional linear regression method used for data dimensionality reduction failed to capture the geometry structure of data. In order to solve this problem

we present a local structure preserving based data dimensionality reduction. A nonnegative least squares method is introduced to construct the graph

which describe the local neighborhood geometry structure information. For the integrating of global structural information and local structure information integration

we also introduce a new model selection method for model parameter estimation

greatly reducing the cost of computation. Experiments on five benchmark datasets(ORL

YaleB

USPS

20Newgroup and Isolet)show that the proposed approach can achieve better results than ULDA

OLDA and NPE.

关键词

Keywords

references

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