基于标记密度分类间隔面的组类属属性学习

类属属性学习避免相同属性预测全部标记，是一种提取各标记独有属性进行分类的一种框架，在多标记学习中得到广泛的应用。而针对标记维度较大、标记分布密度不平衡等问题，已有的基于类属属性的多标记学习算法普遍时间消耗大、分类精度低。为提高多标记分类性能，该文提出一种基于标记密度分类间隔面的组类属属性学习(GLSFL-LDCM)方法。首先，使用余弦相似度构建标记相关性矩阵，通过谱聚类将标记分组以提取各标记组的类属属性，减少计算全部标记类属属性的时间消耗。然后，计算各标记密度以更新标记空间矩阵，将标记密度信息加入原标记中，扩大正负标记的间隔，通过标记密度分类间隔面的方法有效解决标记分布密度不平衡问题。最后，通过将组类属属性和标记密度矩阵输入极限学习机以得到最终分类模型。对比实验充分验证了该文所提算法的可行性与稳定性。

Group-Label-Specific Features Learning Based on Label-Density Classification Margin

The label-specific features learning avoids the same features prediction for all class labels, it is a kind of framework for extracting the specific features of each label for classification, so it is widely used in multi-label learning. For the problems of large label dimension and unbalanced label distribution density, the existing multi-label learning algorithm based on label-specific features has larger time consumption and lower classification accuracy. In order to improve the performance of classification, a Group-Label-Specific Features Learning method based on Label-Density Classification Margin (GLSFL-LDCM) is proposed. Firstly, the cosine similarity is used to construct the label correlation matrix, and the class labels are grouped by spectral clustering to extract the label-specific features of each label group to reduce the time consumption for calculating the label-specific features of all class labels. Then, the density of each label is calculated to update the label space matrix, the label-density information is added to the original label space. The classification margin between the positive and negative labels is expanded, thus the imbalance label distribution density problem is effectively solved by the method of label-density classification margin. Finally, the final classification model is obtained by inputting the group-label-specific features and the label-density matrix into the extreme learning machine. The comparison experiment results verify fully the feasibility and stability of the proposed algorithm.