Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most existing methods undergo high complexity, preventing them from being applied to large-scale tasks. Multi-view clustering via matrix factorization is a representative to address this issue. However, most of them map the data matrices into a fixed dimension, limiting the model's expressiveness. Moreover, a range of methods suffers from a two-step process, i.e., multimodal learning and the subsequent $k$-means, inevitably causing a sub-optimal clustering result. In light of this, we propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework. Specifically, we first project original data matrices into various latent spaces to attain comprehensive information and auto-weight them in a self-supervised manner. Then we directly use the information matrices under diverse dimensions to obtain consensus discrete clustering labels. The unified work of representation learning and clustering boosts the quality of the final results. Furthermore, we develop an efficient optimization algorithm with proven convergence to solve the resultant problem. Comprehensive experiments on various datasets demonstrate the promising clustering performance of our proposed method.

翻译：多视图聚类因其能够利用视图间的一致性与互补信息而受到广泛关注。尽管近年来取得了巨大进展，但大多数现有方法复杂度较高，难以应用于大规模任务。基于矩阵分解的多视图聚类是解决该问题的代表性方法。然而，大多数此类方法将数据矩阵映射到固定维度，限制了模型的表达能力。此外，一系列方法采用两步式流程，即多模态学习与后续的$k$-means聚类，不可避免地导致次优的聚类结果。鉴于此，我们提出一种一步式多样化表示的多视图聚类方法，将多视图学习与$k$-means整合到统一框架中。具体而言，我们首先将原始数据矩阵投影到多种潜在空间以获取全面信息，并以自监督方式自动为其加权。随后，我们直接利用不同维度下的信息矩阵获得一致的离散聚类标签。表示学习与聚类的统一工作提升了最终结果的质量。此外，我们开发了一种具有收敛性保障的高效优化算法来求解该问题。在多个数据集上的综合实验表明，我们所提方法具有优越的聚类性能。