Rank Consistency based Multi-View Learning

Abstract

Complex media objects are often described by multi-view feature groups collected from diverse domains or information channels. Multi-view learning, which attempts to exploit the relationship among multiple views to improve learning performance, has drawn extensive attention. It is noteworthy that in some real-world applications, features of different views may come from different private data repositories, and thus, it is desired to exploit view relationship with data privacy preserved simultaneously. Existing multi-view learning approaches such as subspace methods and pre-fusion methods are not applicable in this scenario because they need to access the whole features, whereas late-fusion approaches could not exploit information from other views to improve the individual view-specific learners. In this paper, we propose a novel multi-view learning framework which works in a hybrid fusion manner. Specifically, we convert predicted values of each view into an Accumulated Prediction Matrix (APM) with low-rank constraint enforced jointly by the multiple views. The joint low-rank constraint enables the view-specific learner to exploit other views to help improve the performance, without accessing the features of other views. Thus, the proposed RANC framework provides a privacy-preserving way for multi-view learning. Furthermore, we consider variants of solutions to achieve rank consistency and present corresponding methods for the optimization. Empirical investigations on real datasets show that the proposed method achieves state-of-the-art performance on various tasks.