In this paper, for sparse multi-label data, based on inter-instance relations and inter-label correlation, a Sparse Multi-Label Kernel Gaussian Neural Network (SMLKGNN) framework is proposed. Double insurance for the sparse multi-label datasets is constructed with bidirectional relations such as inter-instance and inter-label. When instance features or label sets are too sparse to be extracted effectively, we argument that the inter-instance relations and inter-label correlation can supplement and deduce the relevant information. Meanwhile, to enhance the explainable of neural network, Gaussian process is adopted to simulate the real underlying distribution of multi-label dataset. Besides, this paper also considers that contributions of different features have different effects on the experimental results, thus self-attention is leveraged to balance various features. Finally, the applicability of the algorithm is verified in three sparse datasets, and the generalization performance is also validated in three groups of benchmark datasets.

在本文中，针对稀疏多标签数据，基于实例间关系和标签间相关性，提出了一种稀疏多标签核高斯神经网络（SMLKGNN）框架。稀疏多标签数据集的双重保险是用实例间和标签间等双向关系构建的。当实例特征或标签集太稀疏而无法有效提取时，我们认为实例间关系和标签间相关性可以补充和推导相关信息。同时，为了增强神经网络的可解释性，采用高斯过程来模拟多标签数据集的真实底层分布。此外，本文还考虑到不同特征的贡献对实验结果有不同的影响，因此，self-attention 被用来平衡各种特征。最后在三个稀疏数据集上验证了算法的适用性，并在三组基准数据集上验证了泛化性能。