Multiview subspace clustering via low-rank correlation analysis

In order to explore multi-view data, existing low-rank-based multi-view subspace clustering methods seek a common low-rank structure from different views. However, in real-world scenarios, each view will often hold complex structures resulting from noise or outliers, causing unreliable and imprecise graphs, which the previous methods cannot effectively ameliorate. This study proposes a new method based on low-rank correlation analysis to overcome these limitations. Firstly, the canonical correlation analysis strategy is introduced to jointly find the low-rank structures in different views. In order to facilitate a robust solution, a dual regularisation term is further introduced to find such low-rank structures that maximise the correlation in respective views much better. Thus, a unifying clustering structure is then integrated into the model to characterise the connections between different views adaptively. In this way, noise suppression is achieved more effectively. Furthermore, we avoid the uncertainty of spectral post-processing of the unifying clustering structure by imposing a rank constraint on its Laplacian matrix to obtain the clustering results explicitly, further enhancing computation efficiency. Experimental results obtained from several clustering and classification experiments performed using 3Sources, Caltech101-20, 100leaves, WebKB, and Hdigit datasets reveal the proposed method's superiority over compared state-of-the-art methods in Accuracy, Normalised Mutual Information, and F-score evaluation metrics.