He Zhi-Fen, Zhang Chun-Hua, Liu Bin, Li Bo
School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang, 330063 China.
Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang, 330063 China.
Appl Intell (Dordr). 2023;53(8):9444-9462. doi: 10.1007/s10489-022-03945-y. Epub 2022 Aug 9.
Multi-view multi-label learning (MVML) is an important paradigm in machine learning, where each instance is represented by several heterogeneous views and associated with a set of class labels. However, label incompleteness and the ignorance of both the relationships among views and the correlations among labels will cause performance degradation in MVML algorithms. Accordingly, a novel method, - - (MV2ML), is proposed in this paper. First, a label correlation-guided binary classifier kernel-based is constructed for each label. Then, we adopt the multi-kernel fusion method to effectively fuse the multi-view data by utilizing the individual and complementary information among multiple views and distinguishing the contribution difference of each view. Finally, we propose a collaborative learning strategy that considers the exploitation of asymmetric label correlations, the fusion of multi-view data, the recovery of incomplete label matrix and the construction of the classification model simultaneously. In such a way, the recovery of incomplete label matrix and the learning of label correlations interact and boost each other to guide the training of classifiers. Extensive experimental results demonstrate that MV2ML achieves highly competitive classification performance against state-of-the-art approaches on various real-world multi-view multi-label datasets in terms of six evaluation criteria.
多视图多标签学习（MVML）是机器学习中的一个重要范式，其中每个实例由多个异构视图表示，并与一组类别标签相关联。然而，标签不完整性以及对视图之间关系和标签之间相关性的忽视会导致MVML算法的性能下降。因此，本文提出了一种新颖的方法——多视图多标签多任务学习（MV2ML）。首先，为每个标签构建一个基于标签相关性引导的二分类器内核。然后，我们采用多核融合方法，通过利用多个视图之间的个体和互补信息并区分每个视图的贡献差异，有效地融合多视图数据。最后，我们提出一种协同学习策略，该策略同时考虑利用不对称标签相关性、融合多视图数据、恢复不完整标签矩阵以及构建分类模型。通过这种方式，不完整标签矩阵的恢复和标签相关性的学习相互作用并相互促进，以指导分类器的训练。大量实验结果表明，在六个评估标准方面，MV2ML在各种真实世界的多视图多标签数据集上与最先进的方法相比具有极具竞争力的分类性能。
