What question did this study set out to answer?

The aim is to improve multi-view clustering by balancing local complementarity and global consistency across views.

March 12, 2026Open Access

Global Anchor-Guided Local Anchor Learning for Multi-View Clustering

Key Points

The aim is to improve multi-view clustering by balancing local complementarity and global consistency across views.
Developed GL4-MVC, a dual-level anchor graph learning framework.
Constructed anchor graphs integrating local and global anchor guidance.
Utilized an orthogonal mapping matrix for cross-level alignment.
GL4-MVC demonstrated improved performance in multi-view clustering tasks.
It proved scalable and effective for handling large-scale data sets.

Abstract

Multi-view clustering (MVC) is crucial for exploiting complementary information from multi-view data. Anchor-based MVC methods are efficient for large-scale tasks but lack the ability to balance view-specific local complementarity and cross-view global consistency. To address this issue, we propose GL4-MVC, a dual-level anchor graph learning framework. It constructs anchor graphs with integrated adaptive learning of view-specific local anchors and concatenated a priori cross-view global anchor guidance, with an orthogonal mapping matrix enabling cross-level alignment to ensure effective guidance of global information for local learning. GL4-MVC is scalable and suitable for large-scale data. Extensive experimental results confirm the effectiveness and efficiency of GL4-MVC.

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