[1]王 芃,吕 静,沈华乐.基于局部结构保持的自适应有序回归学习[J].南京师范大学学报(自然科学版),2019,42(02):9-16.[doi:10.3969/j.issn.1001-4616.2019.02.002]
 Wang Peng,L Jing,Shen Huale.Improved Adaptive Ordinal Regression Learning Based on Locality Structure Preserving[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(02):9-16.[doi:10.3969/j.issn.1001-4616.2019.02.002]
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基于局部结构保持的自适应有序回归学习
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《南京师范大学学报》(自然科学版)[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年02期
页码:
9-16
栏目:
·数学与计算机科学·
出版日期:
2019-06-30

文章信息/Info

Title:
Improved Adaptive Ordinal Regression Learning Based on Locality Structure Preserving
文章编号:
1001-4616(2019)02-0009-08
作者:
王 芃吕 静沈华乐
南京师范大学计算机科学与技术学院,江苏 南京 210046
Author(s):
Wang PengLü JingShen Huale
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210046,China
关键词:
图像分类有序回归结构保持模糊自适应
Keywords:
image classificationordinal regressionstructure preservingfuzzy adaptive
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2019.02.002
文献标志码:
A
摘要:
有序回归学习是一种在训练模型过程中保持数据间序关系的机器学习方法,在图像分类等领域有着广泛的应用. 现有的有序回归模型通过先验知识获得了更优的性能,但是它们没有考虑数据内的局部结构信息. 本文在有序回归学习的同时保持局部结构信息,并嵌入图像空间距离度量信息,提出了一种基于局部结构保持的自适应有序回归方法(SaLSP-LDLOR). 通过对局部保持矩阵进行模糊自适应处理,获得了更好的鲁棒性. 实验结果表明,SaLSP-LDLOR在有序图像分类的场景下具有更优的性能和良好的鲁棒性.
Abstract:
The ordinal regression learning is a kind of machine learning which preserves the order relations between data. It is widely used in image classification and other fields. Usually,there is some prior knowledge in the ordinal regression model,but the local structure information is not considered. Exploring such information can help to improve the effectiveness of classifiers. In this paper,we propose an improved adaptive ordinal regression method which is based on locality preserving structure(SaLSP-LDLOR),and the newly developed method considers embedding the spatial distance measurement information of the image. Experimental results with the standard data sets verify the effectiveness and the robustness of the proposed method.

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备注/Memo

备注/Memo:
收稿日期:2018-11-23.
基金项目:国家自然基金项目(61876087)、赛尔网络下一代互联网技术创新项目资助(NGII20170524)、江苏省高校自然科学研究项目(18KJB520027).
通讯联系人:吕静,博士研究生,讲师,研究方向:机器学习、模式识别. E-mail:jinglv@njnu.edu.cn
更新日期/Last Update: 2019-06-30