Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification

• URL: http://arxiv.org/abs/2409.07931v1
• Date: Thu, 12 Sep 2024 10:56:11 GMT
• Title: Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification
• Authors: Xiaohuan Lu, Lian Zhao, Wai Keung Wong, Jie Wen, Jiang Long, Wulin Xie,
• Abstract summary: We present a task-augmented cross-view imputation network (TACVI-Net) for handling partial multi-view incomplete multi-label classification. In the first stage, we leverage the information bottleneck theory to obtain a discriminative representation of each view. In the second stage, an autoencoder based multi-view reconstruction network is utilized to extract high-level semantic representation.
• Score: 25.764838008710615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real-world scenarios, multi-view multi-label learning often encounters the challenge of incomplete training data due to limitations in data collection and unreliable annotation processes. The absence of multi-view features impairs the comprehensive understanding of samples, omitting crucial details essential for classification. To address this issue, we present a task-augmented cross-view imputation network (TACVI-Net) for the purpose of handling partial multi-view incomplete multi-label classification. Specifically, we employ a two-stage network to derive highly task-relevant features to recover the missing views. In the first stage, we leverage the information bottleneck theory to obtain a discriminative representation of each view by extracting task-relevant information through a view-specific encoder-classifier architecture. In the second stage, an autoencoder based multi-view reconstruction network is utilized to extract high-level semantic representation of the augmented features and recover the missing data, thereby aiding the final classification task. Extensive experiments on five datasets demonstrate that our TACVI-Net outperforms other state-of-the-art methods.

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