In recent years, Multi-View Clustering (MVC) has attracted increasing attention for its potential to reduce the annotation burden associated with large datasets. The aim of MVC is to exploit the inherent consistency and complementarity among different views, thereby integrating information from multiple perspectives to improve clustering outcomes. Despite extensive research in MVC, most existing methods focus predominantly on harnessing complementary information across views to enhance clustering effectiveness, often neglecting the structural information among samples, which is crucial for exploring sample correlations. To address this gap, we introduce a novel framework, termed Structured Latent Representation Learning based Multi-View Clustering method (SLRL). SLRL leverages both the complementary and structural information. Initially, it learns a common latent representation for all views. Subsequently, to exploit the structural information among samples, a k-nearest neighbor graph is constructed from this common latent representation. This graph facilitates enhanced sample interaction through graph learning techniques, leading to a structured latent representation optimized for clustering. Extensive experiments demonstrate that SLRL not only competes well with existing methods but also sets new benchmarks in various multi-view datasets.

翻译：近年来，多视图聚类因其能够减轻大规模数据集的标注负担而受到越来越多的关注。多视图聚类的目标在于利用不同视图之间固有的**一致性**与**互补性**，从而整合多视角信息以提升聚类效果。尽管多视图聚类领域已有广泛研究，但现有方法大多侧重于利用视图间的互补信息来增强聚类性能，往往忽略了样本间的**结构信息**，而该信息对于探索样本关联至关重要。为弥补这一不足，本文提出了一种新颖的框架——基于结构化潜在表示学习的多视图聚类方法（SLRL）。SLRL 同时利用了互补信息与结构信息。首先，该方法为所有视图学习一个公共的潜在表示；随后，为挖掘样本间的结构信息，基于该公共潜在表示构建 k 近邻图。通过图学习技术，该图能够促进增强的样本交互，从而得到适用于聚类的结构化潜在表示。大量实验表明，SLRL 不仅能够与现有方法有效竞争，还在多个多视图数据集上取得了新的性能基准。