TrashToTreasure: An Informative and Interactive Multi-View Classification Framework

As a basic machine learning task, Multi-View Classification (MVC) has garnered considerable attention and achieved great success. However, the existing MVC methods, especially late fusion style ones still suffer from some problems: 1) hidden valuable information is not well exploited; 2) a lack of interaction before decision making. To address these problems, we propose a novel framework named 'TrashtoTreasure' that leverages mutual information to effectively exploit hidden valuable information. Specifically, the framework explicitly disentangles multi-view information into 'useful' components and 'trash' (noisy) components, and further extracts potentially valuable 'treasure' information from the 'trash' components of all views. Additionally, we design a tailored objective function that facilitates the effective separation of 'useful' and 'trash' components, as well as the synergistic extraction of 'treasure' information. This function guides model optimization through triple mutual information constraints. Experimental results on synthetic data and several real-world data sets verified the effectiveness and superiority of the proposed method. The fresh perspective offered by this article may inspire more interesting exploration in this direction.