基于类属特征的多标签流形学习分类方法  

Label-specific feature-based multi-label manifold learning

作　　者：亢浏越 黄睿[1] 孙广玲[1] KANG Liuyue;HUANG Rui;SUN Guangling(School of Communication and Information Engineering,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学通信与信息工程学院,上海200444

出　　处：《上海大学学报（自然科学版）》2021年第3期525-534,共10页Journal of Shanghai University:Natural Science Edition

基　　金：上海市自然科学基金资助项目(16ZR1411100)。

摘　　要：多标签流形学习(multi-label manifold learning,ML^(2))基于特征流形构建标签流形,将标签逻辑值转换为实数值,能更好地反映标签相关性,提高分类性能.但是,ML^(2)与多数多标签分类方法一样,是基于数据的全部特征进行标签预测,没有考虑不同特征对不同类别标签的鉴别能力.因此,提出一种基于类属特征的多标签流形学习分类(label specific feature based multi-label manifold learning,LSF-ML^(2))方法.首先,利用标签数据优化类属特征重要度矩阵,确定类属特征子集;再将子集的特征流形映射到标签空间,使标签从离散型变为数值型;最后,通过多输出回归实现分类.实验结果表明,所提方法性能优于多种多标签分类方法.Multi-label manifold learning(ML^(2))constructs label manifolds based on feature manifolds and converts logical into numeric labels.This can better reflect the correlations between labels and improve classification performance.However,similar to most methods,ML^(2) is based on all features and ignores different discriminabilities when different features are used to classify different labels.Therefore,a method we call label-specific feature-based multi-label manifold learning(LSF-ML^(2))is proposed.First,the labels are used to optimise the feature importance matrix,which can determine the subset of label-specific features.Then,the feature manifold of the subset is mapped to the label space so that the logical labels can be converted into numeric labels.Finally,a multi-output regression is applied for classification.Experimental results show that the proposed method outperforms several existing multi-label classification methods.

关 键 词：多标签学习 分类 类属特征 流形学习 标签相关性 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]