| 基于自适应最近邻的局部保持投影算法 |
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| 引用本文： | 喻军,李志勇,孟金涛.基于自适应最近邻的局部保持投影算法[J].计算机工程与应用,2011,47(28):209-211. |
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| 作者姓名： | 喻军 李志勇 孟金涛 |
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| 作者单位： | 郑州航空工业管理学院数理系,郑州,450015 |
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| 基金项目： | 河南省教育厅自然科学基金(No.2011B110031) |
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| 摘 要： | 局部保持投影(LPP)是一种新的数据降维技术,但其本身是一种非监督学习算法,对于分类问题效果不是太好。基于自适应最近邻,结合LPP算法,提出了一种有监督的局部保持投影算法(ANNLPP)。该方法通过修改LPP算法中的权值矩阵,在降维的同时,增加了类别信息,是一种有监督学习算法。通过二维数据可视化和UMIST、ORL人脸识别实验,表明该方法对于分类问题具有较好的降维效果。
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| 关 键 词： | 监督学习 自适应最近邻 局部保持投影 |
| 修稿时间： | |
Locality preserving projections based on adaptive nearest neighbor |
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| YU Jun,LI Zhiyong,MENG Jintao.Locality preserving projections based on adaptive nearest neighbor[J].Computer Engineering and Applications,2011,47(28):209-211. |
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| Authors: | YU Jun LI Zhiyong MENG Jintao |
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| Affiliation: | YU Jun,LI Zhiyong,MENG Jintao Department of Mathematics and Physics,Zhengzhou Institute of Aeronautical Industry Management,Zhengzhou 450015,China |
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| Abstract: | Locality Preserving Projections algorithm(LPP) is a new dimensionality reduction technique.But it is an unsupervised learning algorithm.It could not process classification effectively.A new supervised learning algorithm is proposed,which combines with adaptive nearest neighbor and LPP.The method modifies the similar measure matrix of LPP.Compared with LPP,because it uses class information fully,the algorithm is a supervised learning.Experimentation based on 2-D visualization and UMIST,ORL face datasets show... |
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| Keywords: | supervised learning adaptive nearest neighbor locality preserving projections
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