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Multi-instance multi-label learning, an extension of multi-instance learning in multi-label classification, has been successfully used in image classification. In existing algorithms, the distribution of instances in bags is generally assumed to be independent of each other, which is difficult to be guaranteed in image classification. Considering instance correlations in a bag, in this paper a novel...
Multi-instance multi-label learning is an extension of multi-instance learning for multi-label classification. In order to select typical instances with high discrimination for multiple labels, the feature selection via Joint L21-norms minimization is introduced in this paper, and a multi-instance multi-label learning algorithm based on feature selection is proposed. All bags are mapped to typical...
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