最大规范化依赖性多标记半监督学习方法

张晨光; 张燕; 张夏欢

doi:10.16383/j.aas.2015.c140893

最大规范化依赖性多标记半监督学习方法

doi: 10.16383/j.aas.2015.c140893

1.
海南大学信息科学技术学院海口 570228;
2.
北京凌云光视公司图像处理部北京 100097

基金项目:

国家自然科学基金(11261015),海南省高等学校科学研究项目(Hjkj2012-01)资助

详细信息

作者简介:
张晨光海南大学信息科学技术学院讲师.2009年获得北京工业大学硕士学位.主要研究方向为图像处理,模式识别.E-mail:huzcg@foxmail.com

张夏欢北京凌云光视公司图像处理部图像算法工程师.主要研究方向为图像处理和模式识别.E-mail:zhanggongzi@yahoo.cn

通讯作者:
张燕海南大学信息科学技术学院讲师.主要研究方向为数据分析和数据挖掘.本文通信作者.E-mail:zhangyanouc@sina.com

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出版历程
- 收稿日期: 2015-01-13
- 修回日期: 2015-05-06
- 刊出日期: 2015-09-20

Normalized Dependence Maximization Multi-label Semi-supervised Learning Method

1.
College of Information Science and Technology, Hainan University, Haikou 570228;
2.
Department of Image Processing, Luster LightTec, Beijing 100097

Funds:

Supported by National Natural Science Foundation of China (11261015), College Scientific Research Program of Hainan Province of China (Hjkj2012-01)

摘要

摘要: 针对现有多标记学习方法大多属于有监督学习方法, 而不能有效利用相对便宜且容易获得的大量未标记样本的问题, 本文提出了一种新的多标记半监督学习方法, 称为最大规范化依赖性多标记半监督学习方法(Normalized dependence maximization multi-label semi-supervised learning method). 该方法将已有标签作为约束条件,利用所有样本, 包括已标记和未标记样本,对特征集和标签集的规范化依赖性进行估计, 并以该估计值的最大化为目标, 最终通过求解带边界的迹比值问题为未标记样本打上标签. 与其他经典多标记学习方法在多个真实多标记数据集上的对比实验表明, 本文方法可以有效从已标记和未标记样本中学习, 尤其是已标记样本相对稀少时,学习效果得到了显著提高.
- 规范化依赖性 /
- 多标记学习 /
- 半监督学习 /
- 迹比值
Abstract: In view of the problems that most of present multi-label learning methods are supervised learning methods and cannot effectively make use of relatively inexpensive and easily obtained large number of unlabeled samples, this paper puts forward a new multi-label semi-supervised learning method, called normalized dependence maximization multi-label semi-supervised learning method (DMMS). The DMMS regards labeled samples as constraint conditions, estimates the normalized dependency of feature and label sets on all samples including labeled and unlabeled samples, and maximizes the estimation by finally addressing a trace ratio optimization problem with constraint conditions for label unlabeled samples. Experiments comparing DMMS with the state-of-the-art multi-label learning approaches on several real-world datasets show that the DMMS can effectively learn from labeled and unlabeled samples, especially when the labeled is relatively rare, the learning performance can be improved greatly.
- Normalized dependence /
- multi-label learning /
- semi-supervised learning /
- trace ratio

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