Graph-Free Multiview Clustering With Anchors

Multiview clustering aims to detect the consistent graph structure across each view, and has garnered extensive attention in recent years. However, the graph quality determines the clustering performance and cannot be updated in most methods, while the label extraction also relies on post-processing (e.g. spectral rotation). As a consequence, multiple objective functions are optimized independently, making it difficult to achieve one-pass clustering. Motivated by this, Graph-Free Multiview Clustering with Anchor (GMCA) is proposed towards coherence (in optimization), simplicity (in hyperparameters). Considering that similarity only exist among samples in the same cluster for ideal assignments, a label-based reverse framework is proposed for the first time to achieve feature approximation via graph-free factorization. Although non-negative label matrix exhibits indicative interpretability, cluster independence is overlooked, thus non-negative and orthogonal constraint is further imposed and proved beneficial for ideal graph backtracking. Besides, to prevent optimization from focusing on extreme approximating loss caused by redundant features, the Frobenius norm is employed, while allocating flexible view weights as collateral benefit. Comparison experiments with fourteen state-of-the-art methods are performed on eight real-world data sets, while our method exceeds the second-best method by up to$4.85\%$in clustering accuracy and keeps running time linear with samples number.