Low-rank representation induced missing-view recovery for incomplete multi-view clustering

Abstract

In practical applications, multi-view data suffers from incompleteness, which refers to the absence of some samples in each view. Incomplete multi-view clustering is developed to capture the common cluster structure across incomplete views. However, some existing IMC methods ignore the missing samples or the latent relationship between the available and the unavailable samples. We propose a novel missing-view recovery strategy to overcome these two drawbacks. Considering that each missing sample and part of the observable samples belong to the same category, we recover each missing sample via the linear combination of observed samples within each view. Based on the semantic consistency, we extract a unified self-representation graph from complete views and impose a low-rank constraint on it. Furthermore, we learn a comprehensive representation from complete multi-view data and introduce the dynamic weights to consider the importance of various views. The experimental results on several datasets demonstrate that the proposed method outperforms the state-of-the-art IMC approaches.