[1]魏彩锋,孙永聪,曾宪华.图正则化字典对学习的轻度认知功能障碍预测[J].智能系统学报,2019,14(02):369-377.[doi:10.11992/tis.201709033]
 WEI Caifeng,SUN Yongcong,ZENG Xianhua.Dictionary pair learning with graph regularization for mild cognitive impairment prediction[J].CAAI Transactions on Intelligent Systems,2019,14(02):369-377.[doi:10.11992/tis.201709033]
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图正则化字典对学习的轻度认知功能障碍预测(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年02期
页码:
369-377
栏目:
出版日期:
2019-03-05

文章信息/Info

Title:
Dictionary pair learning with graph regularization for mild cognitive impairment prediction
作者:
魏彩锋12 孙永聪12 曾宪华12
1. 重庆邮电大学 计算机科学与技术学院, 重庆 400065;
2. 重庆邮电大学 计算智能重庆市重点实验室, 重庆 400065
Author(s):
WEI Caifeng12 SUN Yongcong12 ZENG Xianhua12
1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunication, Chongqing 400065, China;
2. Chongqing Key Laboratory of Computation Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
关键词:
图正则化字典对学习几何近邻关系图像分类轻度认知功能障碍预测
Keywords:
graph regularizationdictionary pair learninggeometric neighborhood relationshipimage classificationmild cognitive impairment prediction
分类号:
TP391;R749
DOI:
10.11992/tis.201709033
摘要:
针对字典对学习(DPL)方法只考虑了同类子字典的重构误差和不同类表示系数的稀疏性,没有考虑图像间的几何近邻拓扑关系的问题。通过近邻保持使得在同类近邻投影系数之间的距离较小,而不同类投影系数之间的距离大,能够有效提高字典对学习算法的分类性能,基于此提出了基于几何近邻拓扑关系的图正则化的字典对学习(GDPL)算法。在ADNI1数据集上对轻度认知功能障碍预测的实验表明,使用GDPL算法学习的编码系数作为特征预测的准确率(ACC)和ROC曲线下的面积(AUC)比使用结合生物标志作为特征预测的准确率提高了2%~6%,使用GDPL算法比DPL算法的实验结果也有提高。
Abstract:
Aiming at dictionary pair learning (DPL) methods only consider the reconstruction error of a sub-dictionary from the same class and the sparseness of coefficients from different classes, and do not consider the geometric neighborhood topological relationships between images. To improve the classification ability of DPL algorithms, we propose a DPL with graph regularization (GDPL) algorithm based on geometric neighborhood topological relationships. This algorithm is based on the idea that keeping the neighborhood relationship makes the distance between the neighborhood projection coefficients of the same kind small, while the distance between projection coefficients of different kinds is large. Experiments on mild cognitive impairment prediction using the ADNI1 dataset show that the coding coefficient learned from the GDPL algorithm is 2%~6% higher than that which uses the combined biomarker as feature prediction, according to accuracy (ACC) and area under curve (AUC) metrics. Moreover, the experimental result obtained using GDPL is also better than that obtained using DPL algorithm.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-09-16。
基金项目:国家自然科学基金项目(61672120);重庆市科委基础学科和前沿技术研究一般项目(cstc2015jcyjA40036,cstc2014jcyjA40049).
作者简介:魏彩锋,女,1989年生,硕士研究生,主要研究方向为字典学习、图像分类。;孙永聪,男,1991年生,硕士研究生,主要研究方向为稀疏编码、图像检索。;曾宪华,男,1973年生,教授,博士,中国计算机学会会员,主要研究方向为流形学习、计算机视觉。主持国家自然科学基金、重庆自然科学基金等省级以上项目5项。发表学术论文30余篇。
通讯作者:曾宪华.E-mail:zengxh@cqupt.edu.cn
更新日期/Last Update: 2019-04-25