Multi-view Clustering (MVC) has achieved significant progress, with many
efforts dedicated to learn knowledge from multiple views. However, most
existing methods are either not applicable or require additional steps for
incomplete multi-view clustering. Such a limitation results in poor-quality
clustering performance and poor missing view adaptation. Besides, noise or
outliers might significantly degrade the overall clustering performance, which
are not handled well by most existing methods. Moreover, category information
is required in most existing methods, which severely affects the clustering
performance. In this paper, we propose a novel unified framework for incomplete
and complete MVC named self-learning symmetric multi-view probabilistic
clustering (SLS-MPC). SLS-MPC proposes a novel symmetric multi-view probability
estimation and equivalently transforms multi-view pairwise posterior matching
probability into composition of each view's individual distribution, which
tolerates data missing and might extend to any number of views. Then, SLS-MPC
proposes a novel self-learning probability function without any prior knowledge
and hyper-parameters to learn each view's individual distribution from the
aspect of consistency in single-view, cross-view and multi-view. Next,
graph-context-aware refinement with path propagation and co-neighbor
propagation is used to refine pairwise probability, which alleviates the impact
of noise and outliers. Finally, SLS-MPC proposes a probabilistic clustering
algorithm to adjust clustering assignments by maximizing the joint probability
iteratively, in which category information is not required. Extensive
experiments on multiple benchmarks for incomplete and complete MVC show that
SLS-MPC significantly outperforms previous state-of-the-art methods.

本文提出了一种名为 SLS-MPC 的自学习对称多视图概率聚类方法，它使用了对称多视图概率估计和自学习概率函数来实现任意视图数的数据缺失容忍，同时使用了图上下文感知的细化来消除噪声和离群值的影响，并提出了一种概率聚类算法来进行聚类分配调整，无需类别信息，在多个基准测试中明显优于现有的方法。