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[2602.18728] Phase-Consistent Magnetic Spectral Learning for Multi-View Clustering
Summary
This article presents a novel approach to unsupervised multi-view clustering through Phase-Consistent Magnetic Spectral Learning, addressing challenges in cross-view alignment and representation learning.
Why It Matters
The paper tackles significant issues in multi-view clustering, which is crucial for data analysis in various fields. By introducing a method that accounts for directional agreement between views, it enhances the stability and accuracy of clustering, potentially benefiting applications in machine learning and computer vision.
Key Takeaways
- Introduces Phase-Consistent Magnetic Spectral Learning for multi-view clustering.
- Models cross-view directional agreement to improve clustering stability.
- Demonstrates superior performance over existing methods on public benchmarks.
- Utilizes a Hermitian magnetic Laplacian for structured self-supervision.
- Constructs a compact shared structure to enhance input robustness.
Computer Science > Machine Learning arXiv:2602.18728 (cs) [Submitted on 21 Feb 2026] Title:Phase-Consistent Magnetic Spectral Learning for Multi-View Clustering Authors:Mingdong Lu, Zhikui Chen, Meng Liu, Shubin Ma, Liang Zhao View a PDF of the paper titled Phase-Consistent Magnetic Spectral Learning for Multi-View Clustering, by Mingdong Lu and 4 other authors View PDF HTML (experimental) Abstract:Unsupervised multi-view clustering (MVC) aims to partition data into meaningful groups by leveraging complementary information from multiple views without labels, yet a central challenge is to obtain a reliable shared structural signal to guide representation learning and cross-view alignment under view discrepancy and noise. Existing approaches often rely on magnitude-only affinities or early pseudo targets, which can be unstable when different views induce relations with comparable strengths but contradictory directional tendencies, thereby distorting the global spectral geometry and degrading clustering. In this paper, we propose \emph{Phase-Consistent Magnetic Spectral Learning} for MVC: we explicitly model cross-view directional agreement as a phase term and combine it with a nonnegative magnitude backbone to form a complex-valued magnetic affinity, extract a stable shared spectral signal via a Hermitian magnetic Laplacian, and use it as structured self-supervision to guide unsupervised multi-view representation learning and clustering. To obtain robust inputs for spectral ...