Smoothness-Induced Efficient Incomplete Multi-View Clustering

Abstract

Efficient incomplete multi-view clustering has received increasing attention due to its ability to handle large-scale and missing data. Although existing methods have promising performance, 1) they typically generate anchors directly from incomplete and noisy raw data, resulting in uncomprehensive anchor coverage and unreliable results; 2) they typically use only sparse regularization to remove noise and overlook outliers; 3) they ignore the inherent consistency of features in a view. To address these issues, we propose a smoothness-induced efficient incomplete multi-view clustering (SEIC) method. SEIC regards available data as natural anchors selected from complete data, and performs matrix decomposition only on them to obtain reliable small-size representation matrices. View-specific representation matrices are constructed as a tensor to capture consensus and guide matrix decomposition. More significantly, we enforce both smoothness and low-rank coupling on the tensor. Smoothness induces continuous variation of the tensor to further eliminate noise and enhance the relation among features. Benefiting from the noise robustness of SEIC, we design an adaptive noise balance parameter that renders SEIC parameter-free. Furthermore, by constructing a sparse anchor graph on the learned tensor, we propose the spectral clustering version SEIC-SC. Experiments on multiple datasets demonstrate the superior performance and efficiency of SEIC and SEIC-SC.