Weighted Hybrid Fusion with Rank Consistency

Abstract This paper proposes a weighted hybrid multi-view fusion method for the semi-supervised classification problem. Instead of getting access to the features from different views directly, this method utilizes the square losses of the multi-view classifiers to exploit the between-view relationship, which preserves the privacy of data. Considering the different prediction capability of classifiers on multiple views, an objective function with the constraint of rank consistency is constructed to weight view-specific learners adaptively, where the constraint makes each view-specific learner improve its performance by exploring the predicted results of other learners. Furthermore, an iterative algorithm based on the Variant Alternating Splitting Augmented Lagrangian Method (VASALM) and the quadratic programming method is developed to optimize the objective function. Experimental results on different real-world datasets demonstrate the effectiveness of the proposed method for multi-view learning. The experiments also analyze parameter sensitivity and convergency of the optimization algorithm.