New Graph-Guided Contrastive Learning Framework Improves Multi-View Clustering on Incomplete and Noisy Data

Global: New Graph-Guided Contrastive Learning Framework Improves Multi-View Clustering on Incomplete and Noisy Data

A team of machine learning researchers has introduced a novel contrastive learning framework designed to enhance multi-view clustering when data are incomplete or noisy. The work appears in a recent preprint posted on arXiv, aiming to overcome the scarcity of paired samples and the presence of mis‑paired instances that hinder existing methods.

Background: Challenges in Multi-View Clustering

Multi-view clustering (MVC) relies on complementary information across different data modalities. In practice, real‑world datasets often contain missing views or noisy measurements, leading to rare‑paired samples that limit the extraction of shared structure and mis‑paired samples that can misguide learning objectives.

Global‑Graph Guided Contrastive Learning

To address the rare‑paired problem, the authors construct a global‑view affinity graph that connects all samples across views. This graph generates synthetic pairs, enabling the contrastive learning process to explore complementary information more comprehensively than traditional pairwise approaches.

Local‑Graph Weighted Contrastive Learning

For the mis‑paired issue, a local‑graph weighted scheme is introduced. By examining the nearest neighbors of each sample, the method assigns pairwise weights that amplify reliable relationships and diminish potentially erroneous ones, thereby steering the contrastive objective in a more accurate direction.

Unified Imputation‑Free Framework

The two graph‑based components are integrated into a single global‑local architecture that operates without explicit data imputation. This design simplifies deployment and reduces computational overhead while preserving the ability to handle both incomplete and noisy multi‑view scenarios.

Experimental Evaluation

Extensive experiments reported in the preprint evaluate the framework on benchmark datasets under varying levels of incompleteness and noise. Results indicate that the proposed approach consistently outperforms state‑of‑the‑art MVC methods, achieving higher clustering accuracy and robustness.

Potential Impact

If validated on broader applications, this graph‑guided contrastive learning strategy could improve clustering performance in domains such as multimedia analysis, bioinformatics, and sensor fusion, where multi‑view data imperfections are common.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.