Consistency and diversity neural network multi-view multi-label learning

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In multi-view multi-label learning, each object is represented by multiple heterogeneous data and is simultaneously associated with multiple class labels. Previous studies usually use shared subspaces to fuse multi-view representations. However, as the number of views increases, it is more challenging to capture the high-order relationships among multiple views. Therefore, a novel neural network multi-view multi-label learning framework is proposed, which is intended to solve the problem of consistency and diversity among views through a simple and effective method(CDMM). First, we build a separate classifier for each view based on the neural network method of the nonlinear kernel mapping function and require each view to learn a consistent label result. Then, we consider the diversity of individual views while learning a consistent representation among views. For this reason, we combine the Hilbert–Schmidt Independence Criterion with exploring the diversity among different views. Finally, the label correlation factor is in addition to the classification model, and the view contribution factor is added to the prediction model. A large number of comparative experiments with existing state-of-the-art solutions on benchmark multi-view multi-label learning data sets show the effectiveness of this method.