[1]孙晓龙,王靖,杜吉祥.面向缺失像素图像集的修正拉普拉斯特征映射算法[J].华侨大学学报(自然科学版),2017,38(6):862-867.[doi:10.11830/ISSN.1000-5013.201512067]
　SUN Xiaolong,WANG Jing,DU Jixiang.Modified Laplacian Eigenmap Algorithm for Missing Pixels Image Set[J].Journal of Huaqiao University(Natural Science),2017,38(6):862-867.[doi:10.11830/ISSN.1000-5013.201512067]
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面向缺失像素图像集的修正拉普拉斯特征映射算法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第38卷
期数:
2017年第6期
页码:
862-867
栏目:
出版日期:
2017-11-20

文章信息/Info

Title:
Modified Laplacian Eigenmap Algorithm for Missing Pixels Image Set
文章编号:
1000-5013(2017)06-0862-06
作者:
孙晓龙;  王靖;  杜吉祥
华侨大学 计算机科学与技术学院, 福建 厦门 361021
Author(s):
SUN Xiaolong;  WANG Jing;  DU Jixiang
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
关键词:
流形学习;  缺失像素;  拉普拉斯特征映射;  余弦相似度
Keywords:
manifold learning;  missing pixels;  laplacian eigenmaps;  cosine similarity
分类号:
TP181
DOI:
10.11830/ISSN.1000-5013.201512067
文献标志码:
A
摘要:
针对缺失像素图像集,提出修正的拉普拉斯特征映射算法.该算法将缺失像素图像集看成向量集,利用向量之间的余弦相似度衡量缺失像素图像之间的距离,提出一种新的权值构造函数,并在多组标准测试数据集上进行实验.结果表明:修正的拉普拉斯特征映射算法可以很好地挖掘缺失像素图像数据集的内在流形结构,减弱缺失像素带来的不良影响.
Abstract:
In this paper, we propose a modified laplacian eigenmaps algorithm for the missing pixel images. The algorithm takes the missing pixel image set as a vector set, then using the cosine similarity between vectors to measure the distance between missing pixel images. Further, a new weight constructor function is proposed. Experiments on several sets of standard test data sets show that the modified laplacian eigenmaps algorithm can well excavate the intrinsic manifold structure of the missing pixel images and weaken the negative effects of missing pixels.

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备注/Memo

备注/Memo:
收稿日期: 2015-12-28
通信作者: 王靖(1981-),男,教授,博士,主要从事模式识别、推荐系统的研究.E-mail:wroaring@hqu.edu.cn.
基金项目: 国家自然科学基金资助项目(61370006); 福建省自然科学基金资助项目(2014J01237); 福建省教育厅科技项目(JA12006); 福建省高等学校新世纪优秀人才支持计划(2012FJ-NCET-ZR01); 华侨大学中青年教师科技创新资助计划(ZQN-PY116)
更新日期/Last Update: 2017-11-20