Although previous graph-based multi-view clustering algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view-consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the k-means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this paper presents an efficient multi-view clustering approach via unified and discrete bipartite graph learning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view-consensus bipartite graph with adaptive weight learning. Further, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://github.com/huangdonghere/UDBGL.
翻译:尽管基于图的多视角聚类算法已经取得了显著的进展,但它们仍然面临三个限制。首先,它们常常受到高计算复杂度的限制,这限制了它们在大规模场景下的应用。其次,它们通常在单视图级别或视图一致性级别上执行图形学习,但往往忽略了单视图和一致性图形的联合学习的可能性。第三,它们中的许多都依赖于k-means来离散化谱嵌入,缺乏直接学习具有离散聚类结构的图形的能力。因此,本文提出了一种通过联合离散双分图学习(UDBGL)的效率多视角聚类方法。具体而言,将基于锚点的子空间学习并入多个视图中学习视图特定的双分图,其上利用双分图融合来学习具有自适应权重学习的视图一致性双分图。进一步,在保证融合的双分图具有离散聚类结构(具有特定数量的连接分量)的前提下,还施加了拉普拉斯秩约束。通过同时将视图特定的双分图学习,视图一致性双分图学习和离散聚类结构学习整合到一个统一的目标函数中,并设计一个高效的最小化算法来处理这个优化问题,而不需要额外的划分,从而直接实现离散聚类解决方案,注意它在数据大小上具有线性时间复杂度。在各种多视图数据集上的实验结果表明了我们UDBGL方法的鲁棒性和效率。该代码可在https://github.com/huangdonghere/UDBGL上获得。