Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning

Abstract

With the increasing availability of multiview nonnegative data in real applications, multiview representation learning based on nonnegative matrix factorization (NMF) has attracted more and more attentions. However, existing NMF-based methods are sensitive to noises and are difficult to generate discriminative features with noisy views. To address these problems, we propose a co-regularized multiview nonnegative matrix factorization method with correlation constraint for nonnegative representation learning, which jointly exploits consistent and complementary information across different views. Different from previous works, we aim at integrating information from multiple views efficiently and making it more robust to the presence of noisy views. More specifically, we exploit the complementary information of multiple views through the co-regularization to accommodate the presence of the noisy views. Meanwhile, correlation constraint is imposed on the low-dimensional space to learn a common latent representation shared by different views. For the induced objective function, we derive an alternative algorithm to solve the optimization problem. The experimental results on four real datasets demonstrate the effectiveness and robustness of the proposed algorithm.