Consensus graph learning for auto-weighted multi-view projection clustering

Graph-based learning approaches have achieved remarkable success in clustering preva-lent multi-view data owing to their capacities to reveal the relation between data and dis-cover its underlying structure. However, real multi-view data is not only simply high -dimensional, but also contains noise and redundant information, so the learned affinity graphs may be unreliable, let alone optimal, and produce inaccurate clustering results. Moreover, existing graph learning based multi-view projection models only learn a com-mon graph or a shared low-dimensional embedding matrix, which fails to preserve the flexible local manifold geometry of each view. To alleviate these problems, a novel consensus graph-based auto-weighted multi-view projection clustering (CGAMPC) is developed, which performs dimensionality reduction, manifold structure preservation and consensus structured graph learning simultaneously. To be specific, the "2;1-norm is leveraged to resist noise and adaptively select discriminative features. Meanwhile, to preserve the manifold structure information of all views, we construct informative similarity graphs for the projection data, and fuse them into a consensus structured graph via an auto-weighted syn-thesis strategy. Furthermore, an effective alternating iterative algorithm is presented to optimize our CGAMPC. Finally, numerical studies on several multi-view benchmark data-sets justify the superiority of the proposed approach over other state-of-the-art clustering approaches. (C) 2022 Elsevier Inc. All rights reserved.