上海交通大学学报

上海交通大学学报（自然版） ›› 2013, Vol. 47 ›› Issue (07): 1082-1087.

• 自动化技术、计算机技术 • 上一篇    下一篇

局部测地距离估计的增量等距特征映射算法

吴文通1，李元祥1，韦邦合2，郑思龙1   

  1. (1. 上海交通大学 航空航天学院， 上海 200240； 2. 空军94969部队保障部， 上海 200436)
  • 收稿日期:2012-08-27 出版日期:2013-07-30 发布日期:2013-07-30
  • 基金资助:

    国家自然科学基金资助项目（41174164，61174196）

Incremental ISOMAP Method Based on Locally Estimated Geodesic Distance

WU Wentong1,LI Yuanxiang1，Wei Banghe2,Zheng Silong1
  

  1. (1. School of Aeronautics and Astronautics, Shanghai Jiaotong University, Shanghai 200240, China;2. Guarantee Department of Unit 94969, The Air Force, Shanghai 200436, China)
     
  • Received:2012-08-27 Online:2013-07-30 Published:2013-07-30

摘要：

基于经典等距特征映射（ISOMAP）算法易受噪声干扰和邻域大小影响，采用局部测地距离估计输入数据点的初始邻域，并结合增量学习思想，提出一种基于局部测地距离估计的增量ISOMAP算法进行降维，以提高ISOMAP算法的分类能力.人脸识别试验表明，该算法识别性能优越，对噪声和几何形变具有鲁棒性.

 
 

关键词: 流形学习, 等距特征映射, 增量学习, 局部测地距离, 降维

Abstract:

The classical ISOMAP (isometric feature mapping) method is prone to suffer from the noise and the size of neighborhood. A novel method called “Incremental ISOMAP” based on locally estimated geodesic distance for dimensionality reduction was presented. First, this method assumed that the neighborhood of a point located at the highly twisted placed of the manifold might not be linear so that its neighbors should be determined by geodesic distance. Then, incremental learning was used to replace the batch mode in pattern recognition,  aiming to enhance the ability of real time. The proposed method is simple, general and easy to deal with high-dimensional data. The experimental results on face recognition show that the method is efficient and robust.
 

Key words: manifold learning, isometric feature mapping （ISOMAP）, incremental learning, local geodesic distance, dimension reduction

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