Phase-Consistent Magnetic Spectral Learning for Multi-View Clustering

TL;DR

This paper introduces a novel spectral learning method for multi-view clustering that models cross-view directional agreement as a phase term, leading to more stable and accurate clustering results.

Contribution

It proposes a phase-consistent magnetic spectral learning approach that explicitly models directional agreement, improving spectral signal stability and clustering performance in multi-view data.

Findings

Outperforms strong baselines on multiple benchmarks

Provides more stable spectral signals under view discrepancy

Effectively suppresses noisy or inconsistent relations

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.