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 for the purpose of handling partial multi-view incomplete multi-label classification. Specifically%2C we employ a two-stage network to derive highly task-relevant features to recover the missing views. In the first stage%2C 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%2C an autoencoder based multi-view reconstruction network is utilized to extract high-level semantic representation of the augmented features and recover the missing data%2C thereby aiding the final classification task. Extensive experiments on five datasets demonstrate that our TACVI-Net outperforms other state-of-the-art methods.?quality=80&w=800)
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