Collaborative Unsupervised Multi-View Representation Learning
Qinghai Zheng, Jihua Zhu, Zhongyu Li
Abstract
In this paper, we delve into the challenging problem in multi-view learning, namely unsupervised multi-view representation learning, the goal of which is to effectively integrate information from multiple views and learn the unified feature representation with comprehensive information in an unsupervised manner. Despite the progress attained in recent years, it is still a challenging issue since the correlations across multiple views are complex and difficult to model during the learning process, especially in the absence of label information. To address this problem, we introduce a novel method, termed Collaborative Unsupervised Multi-view Representation Learning (CUMRL), which benefits from the high-order view correlations of multi-view data by introducing a collaborative learning strategy. Specifically, the low-rank tensor constraint is employed and plays the role of a bridge, which links the view-specific compact learning and unified representation learning in CUMRL. Experiments demonstrate the effectiveness and competitiveness of the multi-view representation achieved by the proposed method for different learning tasks, compared to several state-of-the-art methods.