Phase-Consistent Magnetic Spectral Learning for Multi-View Clustering

• URL: http://arxiv.org/abs/2602.18728v1
• Date: Sat, 21 Feb 2026 06:04:57 GMT
• Title: Phase-Consistent Magnetic Spectral Learning for Multi-View Clustering
• Authors: Mingdong Lu, Zhikui Chen, Meng Liu, Shubin Ma, Liang Zhao,
• Abstract summary: Unsupervised multi-view clustering aims to partition data into meaningful groups by leveraging complementary information from multiple views without labels.<n>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.<n>We propose emphPhase-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.
• Score: 18.462238432927915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Delving into Spectral Clustering with Vision-Language Representations [27.433418706301477]
  We propose Neural Tangent Kernel Spectral Clustering that leverages cross-modal alignment in pre-trained vision-language models.<n>We show that this formulation amplifies within-cluster connections while suppressing spurious ones across clusters.<n>Our method consistently outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2026-02-10T09:36:24Z)
• Deep Incomplete Multi-View Clustering via Hierarchical Imputation and Alignment [15.396375506151102]
  We propose a novel deep IMVC framework that integrates hierarchical imputation and alignment with four key components.<n> Experiments on benchmarks demonstrate that our framework achieves superior performance under varying levels of missingness.
  arXiv  Detail & Related papers  (2026-01-14T00:46:00Z)
• Wasserstein-Aligned Hyperbolic Multi-View Clustering [58.29261653100388]
  This paper proposes a novel Wasserstein-Aligned Hyperbolic (WAH) framework for multi-view clustering.<n>Our method exploits a view-specific hyperbolic encoder for each view to embed features into the Lorentz manifold for hierarchical semantic modeling.
  arXiv  Detail & Related papers  (2025-12-10T07:56:19Z)
• Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence [72.41989962665285]
  Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views.<n>However, real-world scenarios often present a challenge as only partial data is consistently aligned across different views.<n>We design a causal multi-view clustering network, termed CauMVC, to tackle this problem.
  arXiv  Detail & Related papers  (2025-09-19T14:31:40Z)
• Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning [65.75756724642932]
  In incomplete multi-view clustering, missing data induce prototype shifts within views and semantic inconsistencies across views.<n>We propose an IMVC framework, imputation- and alignment-free for consensus semantics learning (FreeCSL)<n>FreeCSL achieves more confident and robust assignments on IMVC task, compared to state-of-the-art competitors.
  arXiv  Detail & Related papers  (2025-05-16T12:37:10Z)
• Multi-level Reliable Guidance for Unpaired Multi-view Clustering [15.600038277342128]
  Unpaired multi-view clustering (UMC) aims to achieve effective joint clustering using unpaired samples observed across multiple views.<n>We propose a novel method, Multi-level Reliable Guidance for UMC (MRG-UMC), which integrates multi-level clustering and reliable view guidance.<n>MRG-UMC outperforms state-of-the-art UMC methods, achieving an average NMI improvement of 12.95% on multi-view datasets.
  arXiv  Detail & Related papers  (2024-07-01T12:49:55Z)
• Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID [56.573905143954015]
  We propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2023-05-22T03:27:46Z)
• Multi-View Clustering via Semi-non-negative Tensor Factorization [120.87318230985653]
  We develop a novel multi-view clustering based on semi-non-negative tensor factorization (Semi-NTF) Our model directly considers the between-view relationship and exploits the between-view complementary information. In addition, we provide an optimization algorithm for the proposed method and prove mathematically that the algorithm always converges to the stationary KKT point.
  arXiv  Detail & Related papers  (2023-03-29T14:54:19Z)
• A Clustering-guided Contrastive Fusion for Multi-view Representation Learning [7.630965478083513]
  We propose a deep fusion network to fuse view-specific representations into the view-common representation. We also design an asymmetrical contrastive strategy that aligns the view-common representation and each view-specific representation. In the incomplete view scenario, our proposed method resists noise interference better than those of our competitors.
  arXiv  Detail & Related papers  (2022-12-28T07:21:05Z)
• Adaptively-weighted Integral Space for Fast Multiview Clustering [54.177846260063966]
  We propose an Adaptively-weighted Integral Space for Fast Multiview Clustering (AIMC) with nearly linear complexity. Specifically, view generation models are designed to reconstruct the view observations from the latent integral space. Experiments conducted on several realworld datasets confirm the superiority of the proposed AIMC method.
  arXiv  Detail & Related papers  (2022-08-25T05:47:39Z)
• Tensor-based Multi-view Spectral Clustering via Shared Latent Space [14.470859959783995]
  Multi-view Spectral Clustering (MvSC) attracts increasing attention due to diverse data sources. New method for MvSC is proposed via a shared latent space from the Restricted Kernel Machine framework.
  arXiv  Detail & Related papers  (2022-07-23T17:30:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.