Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification
Chengliang Liu, Jinlong Jia, Jie Wen, Yabo Liu, Xiaoling Luo, Chao Huang, Yong Xu
Abstract
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.
BibTeX
@article{Liu_Jia_Wen_Liu_Luo_Huang_Xu_2024, title={Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29293}, DOI={10.1609/aaai.v38i12.29293}, abstractNote={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.}, number={12}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Liu, Chengliang and Jia, Jinlong and Wen, Jie and Liu, Yabo and Luo, Xiaoling and Huang, Chao and Xu, Yong}, year={2024}, month={Mar.}, pages={13864-13872} }