Fusion of local and globle structures for manifold learning

HUANG Hong; LI Jian-wei; FENG Hai-liang

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Fusion of local and globle structures for manifold learning

Article | 更新时间：2020-08-12

- Fusion of local and globle structures for manifold learning
- Optics and Precision Engineering Vol. 17, Issue 3, Pages: 626-632(2009)
- 作者机构：
  
  1. 重庆大学光电技术及系统教育部重点实验室重庆,400044
  2. 重庆工学院教授流动站重庆,400050
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.4
- Received：30 March 2008，
  
  Revised：16 May 2008，
  
  Published Online：25 March 2009，
  
  Published：25 March 2009
- 稿件说明：
移动端阅览
黄鸿, 李见为, 冯海亮. 融合局部和全局结构的流形学习[J]. 光学精密工程, 2009,17(3): 626-632

HUANG Hong, LI Jian-wei, FENG Hai-liang. Fusion of local and globle structures for manifold learning[J]. Editorial Office of Optics and Precision Engineering, 2009,17(3): 626-632
黄鸿, 李见为, 冯海亮. 融合局部和全局结构的流形学习[J]. 光学精密工程, 2009,17(3): 626-632 DOI：

HUANG Hong, LI Jian-wei, FENG Hai-liang. Fusion of local and globle structures for manifold learning[J]. Editorial Office of Optics and Precision Engineering, 2009,17(3): 626-632 DOI：

摘要

提出了一种融合局部与全局结构的保持嵌入(LGPE)算法。该方法首先假定目标空间的整体映射函数

然后结合数据的全局几何结构分布信息进行数据重构

最后通过最小化准则函数来得到嵌入高维空间的低维子流形。LGPE方法在保持数据局部结构的同时保留了全局结构信息

在信噪比为10 dB的稀疏Swiss-roll(

=400)和COIL-20多姿态数据集上都取得了较好的维数约简效果。与其他局部流形学习方法相比

该方法在AT

T人脸图像库中

当嵌入特征矢量维数

40时

其识别率提高了约15%。在人工与真实数据库的实验结果表明

本文方法对噪声和稀疏数据具有较好的鲁棒性。

Abstract

A new method called Local and Global Preserving Embedding (LGPE) is proposed for manifold learning. This method assumes a global embedding function in low dimensional space

then incorporates the relative compactness information of the data distributions on the global geometry to reconstruct sample data. Finally

the global low dimensional submanifold is obtained by minimizing the cost function.The LGPE preserves the local and global structures of the data points simultaneously

and can obtain better dimensionality reduction on the sparse Swiss-roll dataset with noises (

=400

SNR=10 dB) and COIL-20 multi-poses dataset.When it is used in the AT

T face dateset

the recognition rate can be improved by 15% as compared with that of other local manifold methods under condition of embedding dimension lower than 40. The experimental results on both synthetic and real data sets show that proposed method is effectiveness and robustness for noise and sparse data.

关键词

Keywords

references

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Related Institution

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重庆大学光电技术及系统教育部重点实验室，重庆 400030

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酒泉卫星发射中心

Key Laboratory of Optoelectronic Technology and System， Ministry of Education， Chongqing University

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