Self-Learning Symmetric Multi-view Probabilistic Clustering

05/12/2023
by   Junjie Liu, et al.
0

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.

READ FULL TEXT
research
07/07/2023

Unpaired Multi-View Graph Clustering with Cross-View Structure Matching

Multi-view clustering (MVC), which effectively fuses information from mu...
research
03/28/2021

Self-supervised Discriminative Feature Learning for Multi-view Clustering

Multi-view clustering is an important research topic due to its capabili...
research
08/05/2022

Localized Sparse Incomplete Multi-view Clustering

Incomplete multi-view clustering, which aims to solve the clustering pro...
research
10/09/2012

Multi-view constrained clustering with an incomplete mapping between views

Multi-view learning algorithms typically assume a complete bipartite map...
research
09/24/2022

Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation

Incomplete Multi-View Clustering aims to enhance clustering performance ...
research
05/05/2022

View-labels Are Indispensable: A Multifacet Complementarity Study of Multi-view Clustering

Consistency and complementarity are two key ingredients for boosting mul...
research
12/05/2021

Contextual Multi-View Query Learning for Short Text Classification in User-Generated Data

Mining user-generated content–e.g., for the early detection of outbreaks...

Please sign up or login with your details

Forgot password? Click here to reset