摘要： 基于单标记分类的降维及特征选择方法难以直接运用到多标记学习中，而将多标记学习问题独立分解为多个单标记学习问题再进行降维会丢失标记的相关性信息。为此，提出一种基于嵌入式特征提取的多标记分类算法，将非负矩阵分解引入到多标记学习过程中，在对原始多标记数据集进行特征提取的同时，减少冗余特征、不相关特征及高维特征对多标记分类的影响。在4个公开的标准数据集上进行对比实验，结果表明该算法能对数据进行有效降维，在准确度、精度、F度量值等评价指标上相比传统BR、CC、LM算法具有更好的分类性能。

关键词: 多标记学习, 非负矩阵分解, 特征提取, 降维, 乘性迭代, 特征转换

Abstract: Dimensionality reduction and feature selection methods based on single-label classification cannot be directly applied to multi-label learning.If a multi-label learning problem is composed into multiple independent single-label learning problems to enable dimensionality reduction,the correlation between labels will be lost.To address the problem,this paper proposes a multi-label classification algorithm based on embedded feature extraction.The algorithm introduces non-negative matrix factorization into multi-label learning.In feature extraction of original multi-label datasets,the redundant or irrelevant features are reduced and effects of high-dimensional features on multi-label classification are relieved.The comparison experiments are performed on four public standard data sets,and results show that the proposed algorithm can effectively reduce dimensions of data,and lead to better classification performance than traditional BR,CC,LM and other algorithms in terms of multiple evaluation indicators,including accuracy,precision and F measure.

Key words: multi-label learning, Non-Negative Matrix Factorization(NMF), feature extraction, dimensionality reduction, multiplicative iteration, feature transform

中图分类号: 

• TP391.4