Phase-Consistent Magnetic Spectral Learning for Multi-View Clustering
arXiv:2602.18728v1 Announce Type: new 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
arXiv:2602.18728v1 Announce Type: new 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 extraction at scale, we construct a compact shared structure with anchor-based high-order consensus modeling and apply a lightweight refinement to suppress noisy or inconsistent relations. Extensive experiments on multiple public multi-view benchmarks demonstrate that our method consistently outperforms strong baselines.
Executive Summary
This article proposes a novel approach to unsupervised multi-view clustering (MVC) called Phase-Consistent Magnetic Spectral Learning (PCMSL). The method explicitly models cross-view directional agreement as a phase term and combines it with a nonnegative magnitude backbone to form a complex-valued magnetic affinity. PCMSL extracts a stable shared spectral signal via a Hermitian magnetic Laplacian and uses it as structured self-supervision to guide unsupervised multi-view representation learning and clustering. The proposed method is demonstrated to be effective in handling view discrepancy and noise, and outperforms strong baselines on multiple public multi-view benchmarks.
Key Points
- ▸ PCMSL explicitly models cross-view directional agreement as a phase term
- ▸ The method combines phase term with nonnegative magnitude backbone to form a complex-valued magnetic affinity
- ▸ PCMSL extracts a stable shared spectral signal via a Hermitian magnetic Laplacian
Merits
Improved Handling of View Discrepancy and Noise
PCMSL's use of phase term and complex-valued magnetic affinity allows it to effectively handle view discrepancy and noise, leading to more robust clustering results.
Effective Self-Supervision
The structured self-supervision provided by the shared spectral signal enables PCMSL to guide unsupervised multi-view representation learning and clustering effectively.
Demerits
Potential Computational Overhead
The construction of compact shared structure with anchor-based high-order consensus modeling and lightweight refinement may introduce additional computational overhead, which could be a limitation in certain applications.
Expert Commentary
While PCMSL shows promising results in handling view discrepancy and noise, its effectiveness in real-world applications depends on various factors, such as data quality and the complexity of the clustering task. Furthermore, the potential computational overhead of the method should be carefully evaluated in different scenarios. Overall, PCMSL is a valuable contribution to the field of unsupervised clustering and representation learning, and its findings have implications for both practical and policy-related applications.
Recommendations
- ✓ Further investigation into the scalability and computational efficiency of PCMSL is recommended, particularly in large-scale clustering tasks.
- ✓ The application of PCMSL to other domains and datasets, such as time-series or network data, could provide valuable insights into its generalizability and effectiveness.
