Few-shot Anchor-guided Multi-View Clustering With Pearson Correlation.

Multi-view clustering (MVC) has emerged as a powerful paradigm for integrating heterogeneous data representations. However, existing multi-view clustering methods typically encounter four specific bottlenecks: the cubic computational explosion inherent to spectral graph methods, the degradation of Euclidean distance in high-dimensional spaces, extreme label scarcity in practical semi-supervised applications, and a heavy reliance on costly manual hyper-parameter tuning. To address these challenges, this paper proposes a novel framework termed Few-shot Anchor-guided Multi-view Clustering with Pearson Correlation (FAMC-PC). Unlike traditional approaches, FAMC-PC introduces a statistical Pearson Correlation metric to construct bipartite anchor graphs, capturing intrinsic structural directionality more effectively than relying on purely distance-based measures. We further propose a unified Non-negative Matrix Factorization (NMF) model that seamlessly integrates consensus graph fusion with sparse few-shot constraints. This mechanism anchors latent representations to scarce labeled data, bridging the gap between unsupervised structure learning and supervised classification without requiring extensive annotations. Notably, FAMC-PC establishes a tuning-free design, substantially reducing the computational burden and labor costs associated with manual hyper-parameter tuning. Extensive experiments on six benchmark datasets demonstrate that FAMC-PC achieves competitive clustering performance and high efficiency compared to nine state-of-the-art baselines, offering a significant advantage in terms of label efficiency. The source code is available at https://github.com/LstinWh/FAMC-PC.