基于双向映射学习的多标签分类算法

现有的多标签学习算法往往只侧重于实例空间到标签空间的正向投影，正向投影时由于特征维数降低所产生的实例空间信息丢失的问题往往被忽略。针对以上问题，提出一种基于双向映射学习的多标签分类算法。首先，利用实例空间到标签空间的正向映射损失建立线性多标签分类模型；然后，在模型中引入重构损失正则项构成双向映射模型，补偿由于正向映射时导致的鉴别信息的丢失；最后，将双向映射模型结合标签相关性和实例相关性充分地挖掘标签之间、实例之间的潜在关系，并利用非线性核映射提高模型对非线性数据的处理能力。实验结果表明，与近年来的其他几种方法相比，该方法在汉明损失、一次错误率和排序损失上的性能平均提升17.68%、17.01%、18.57%;在六种评价指标上的性能平均提升了12.37%,验证了模型的有效性。

Multi-label classification algorithm based on bidirectional mapping learning

The existing multi-label learning algorithms tend to focus on the forward projection from the instance space to the label space, and the problem of the loss of instance space information due to the reduction of the feature dimension during forward projection is often ignored. Hence, this paper proposed a multi-label classification algorithm with bidirectional mapping learning. Firstly, the method used the forward mapping loss from instance space to label space to established a linear multi-label classification model. Second, the bidirectional mapping model based on reconstruction loss regularization compensated for the discriminatory information loss in the forward mapping process. Finally, the bidirectional mapping model combined label correlation and instance correlation fully exploits the potential relationship between labels and instances, and improved the ability of the model to handle nonlinear data through nonlinear kernel mapping. The experimental results show that compared with several other methods in recent years, the average performance improvements of the method in terms of Hamming loss, one error and ranking loss are 17.68%, 17.01% and 18.57%. Average performance improvement of 12.37% on 6 evaluation metrics, which verifies the effectiveness of the proposed method.