Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification
DOI:
https://doi.org/10.1609/aaai.v38i12.29293Keywords:
ML: Multi-instance/Multi-view Learning, ML: Multi-class/Multi-label Learning & Extreme Classification, ML: Multimodal LearningAbstract
As a combination of emerging multi-view learning methods and traditional multi-label classification tasks, multi-view multi-label classification has shown broad application prospects. The diverse semantic information contained in heterogeneous data effectively enables the further development of multi-label classification. However, the widespread incompleteness problem on multi-view features and labels greatly hinders the practical application of multi-view multi-label classification. Therefore, in this paper, we propose an attention-induced missing instances imputation technique to enhance the generalization ability of the model. Different from existing incomplete multi-view completion methods, we attempt to approximate the latent features of missing instances in embedding space according to cross-view joint attention, instead of recovering missing views in kernel space or original feature space. Accordingly, multi-view completed features are dynamically weighted by the confidence derived from joint attention in the late fusion phase. In addition, we propose a multi-view multi-label classification framework based on label-semantic feature learning, utilizing the statistical weak label correlation matrix and graph attention network to guide the learning process of label-specific features. Finally, our model is compatible with missing multi-view and partial multi-label data simultaneously and extensive experiments on five datasets confirm the advancement and effectiveness of our embedding imputation method and multi-view multi-label classification model.Downloads
Published
2024-03-24
How to Cite
Liu, C., Jia, J., Wen, J., Liu, Y., Luo, X., Huang, C., & Xu, Y. (2024). Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 38(12), 13864-13872. https://doi.org/10.1609/aaai.v38i12.29293
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Section
AAAI Technical Track on Machine Learning III