面向多视图聚类的多层级对比谱融合
Multi-Hierarchical Contrastive Spectral Fusion for Multi-View Clustering
把深度谱嵌入直接嵌入编码器以保留局部流形结构，并配合视图级/共识级的多层级对比损失，可作为多视图或多模态特征融合时提升聚类友好性与跨视图一致性的可复用训练范式。
原文依据 MCSF integrates deep spectral embedding into the encoder to preserve local manifold structure
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多视图对比聚类已成为从异构数据源中学习全面表征的一种强大范式。然而，现有方法通常忽略内在的几何结构与聚类结构，使其对结构不敏感。本文提出一个新框架，执行多层级对比谱融合（MCSF）以解决上述局限。MCSF 将深度谱嵌入集成到编码器中以保留局部流形结构，引导所学表征具有聚类友好性。为增强跨视图一致性，MCSF 引入多层级对比损失，联合优化：（1）视图特定的结构保持，（2）视图共识对齐，（3）共识结构精炼。该机制能够构建准确且语义一致的共识表征，有效融合多视图信息并揭示真实的簇结构。在基准上的大量实验验证了多层级对比谱融合在聚类准确率与表征质量上的有效性。
英文原文摘要 1160 字符
Multi-view contrastive clustering has emerged as a powerful paradigm for learning comprehensive representations from heterogeneous data sources. However, prevailing approaches typically overlook the intrinsic geometric and clustering structures, rendering them structure-agnostic. In this paper, we propose a novel framework that performs Multi-Hierarchical Contrastive Spectral Fusion (MCSF) to address these limitations. MCSF integrates deep spectral embedding into the encoder to preserve local manifold structure, guiding the learned representations to be clustering-friendly. To enhance cross-view consistency, MCSF introduces a multi-hierarchical contrastive loss jointly optimizing (1) view-specific structure preservation, (2) view-consensus alignment, and (3) consensus structure refinement. This mechanism enables the construction of an accurate and semantically consistent consensus representation, effectively fusing multi-view information and uncovering authentic cluster structures. Extensive experiments on benchmarks validate the effectiveness of multi-hierarchical contrastive spectral fusion in clustering accuracy and representation quality.