Semi-supervised optimization algorithm based on laplacian eigenmaps

被引：0
作者：
Luo Q. [1 ]
Wen J. [1 ]
Wu Y. [1 ]
Wang M. [1 ]
机构：
[1] The school of Technology, Beijing Forestry University, No.35 Tsinghua East Road, Haidian District, Beijing
来源：
Luo, Qinjuan (luoqinjuan@126.com) | 1600年 / North Atlantic University Union, 942 Windemere Dr. NW.,, Salem, Oregon 97304, United States卷 / 14期
关键词：
Dimensionality reduction; Laplacian eigenmaps; Semi-supervision;
D O I：
10.46300/9106.2020.14.62
中图分类号：
学科分类号：
摘要：
As a member of many dimensionality reduction algorithms, manifold learning is the hotspot of recent dimensionality reduction algorithm. Despite it is good at retaining the original space structure, there is no denying that its effect of classifying still has room for improvement. Based on Laplacian Eigenmap, which is one of the manifold learning algorithm, this paper committed to optimize the algorithm combined with a semi-supervised learning ideas, which can improve the recognition rate. Finally, the better method of two forms is tested in the surface electromyography system and plant leaf identification system. The experimental results show that this semi-supervised method does well in classifying. © 2020, North Atlantic University Union. All rights reserved.
引用
收藏
页码：474 / 481
页数：7
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