基于知识重用的噪声环境谱聚类

doi:10.16451/j.cnki.issn1003-6059.202509002

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Abstract Spectral clustering is widely used in the field of unsupervised learning due to its superiority in modeling pairwise similarity structures within data. However, conventional spectral clustering methods typically rely on the assumption of clean and structurally consistent datasets, and as a result their performance degrades significantly when the samples are noisy or mismatched in real-world applications. To address these issues, an extended spectral clustering framework, noise spectral clustering based on knowledge reuse(NSCR), is proposed. NSCR effectively leverages semantic knowledge from large-scale multi-model neural networks. A pseudo-label generation module is then designed by leveraging the prior capability of multi-modal neural network models in cross-modal understanding. Reliable samples are identified by semantic-consistency verification and saliency-aware confidence modeling. Softmax entropy is introduced as an uncertainty measure to filter pseudo-labels. Semantically consistent samples with low entropy across multiple models are selected and their pseudo-labels are generated to guide the clustering. Moreover, a joint optimization objective is employed to extend the traditional spectral clustering methods. A feature alignment term and a regularization balancing factor are utilized to mitigate semantic conflicts between pseudo-label supervision and clustering objective. Experiments on public datasets demonstrate that NSCR exhibits good robustness and generalization capability.

Key words： Noisy Data Spectral Clustering Knowledge Reuse Pseudo-Label Generation

Received: 21 July 2025

ZTFLH:

TP181.1

Fund:National Natural Science Foundation of China(No.62006131,62071260,62471266), Natural Science Foundation of Zhejiang Province(No.LQ21F020009,LGF21F020008,LQ22F020020), Ningbo 2025 Key Technological Innovation Program (No.2023Z224)

Corresponding Authors: YE Xulun, Ph.D., associate professor. His research interests include Bayesian learning, nonparametric clustering and convex analysis.

About author:: YU Minda, Master student. His research interests include computer vision, unsupervised learning and cluster analysis.

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Cite this article:

YU Minda,YE Xulun. Noise Spectral Clustering Based on Knowledge Reuse[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(9): 778-790.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202509002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I9/778

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