Ma Jianghong, Chow Tommy W S, Zhang Haijun
IEEE Trans Cybern. 2022 Jan;52(1):101-115. doi: 10.1109/TCYB.2020.2977133. Epub 2022 Jan 11.
Multilabel learning focuses on assigning instances with different labels. In essence, the multilabel learning aims at learning a predictive function from feature space to a label space. The predictive function learning procedure can be regarded as a feature selection procedure and as a classifier construction procedure. For feature selection, we extract features for each label based on the learned positive and negative feature-label correlations. The positive and negative relationships can illustrate which labels can and cannot be well presented by the corresponding features, respectively, due to the semantic gap. For classifier construction, we perform sample-specific and label-specific classifications. The interlabel and interinstance correlations are combined in these two kinds of classifications. These two correlations are learned from both input features and output labels when the output labels are too sparse to reveal the informative correlation. However, there exists the semantic gap when combining input and output spaces to mine the labelwise relationship. The semantic gap can be bridged by the learned feature-label correlation. Finally, extensive experimental results on several benchmarks under four domains are presented to show the effectiveness of the proposed framework.
多标签学习专注于为实例分配不同的标签。本质上，多标签学习旨在学习从特征空间到标签空间的预测函数。预测函数的学习过程可被视为一个特征选择过程和一个分类器构建过程。对于特征选择，我们基于学习到的正、负特征 - 标签相关性为每个标签提取特征。由于语义鸿沟，正、负关系可以分别说明哪些标签能够以及不能由相应特征很好地呈现。对于分类器构建，我们进行特定样本和特定标签的分类。这两种分类中结合了标签间和实例间的相关性。当输出标签过于稀疏而无法揭示信息性相关性时，这两种相关性是从输入特征和输出标签中学习得到的。然而，在组合输入和输出空间以挖掘按标签的关系时存在语义鸿沟。可以通过学习到的特征 - 标签相关性来弥合语义鸿沟。最后，给出了在四个领域的几个基准测试上的大量实验结果，以展示所提出框架的有效性。
