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原文摘要

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.