面向动态数据的联合自表示子空间聚类方法

doi:10.16451/j.cnki.issn1003-6059.202501005

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Abstract Self-expressive subspace clustering methods perform well in processing high-dimensional data and are one of the key techniques in this field. However, traditional self-expressive models typically assume that the dataset is static, and it is difficult to adapt to dynamic, continuously arriving data streams. It leads to two issues: feature heterogeneity between novel data and old data and the inclusion of unknown novel classes in newly arriving samples. To address these problems, a subspace clustering framework, joint self-expressive subspace clustering method(JSSC), is proposed. JSSC is specifically designed to handle both old and novel category samples. JSSC adapts to the continuous arrival of data streams by combining a self-expressive feature learning module with a novel category sample processing module. The proposed method effectively clusters novel category samples while maintaining strong performance on existing categories. Additionally, a deep autoencoder is utilized to learn subspace basis. Thus intuitive and interpretable representations are achieved, and the known and emerging categories are simultaneously managed through pairwise objectives and regularization terms. Experimental results on benchmark datasets show that JSSC outperforms current state-of-the-art approaches in clustering tasks, particularly excelling in handling novel categories within dynamic data.

Key words： Subspace Clustering Self-Expressive Learning Subspace Basis Dynamic Data

Received: 04 November 2024

ZTFLH:

TP183

Fund:National Natural Science Foundation of China(No.62006131,62203238,62071260), Natural Science Foundation of Zhejiang Province(No.LZ22F020001,LQ21F020009,LQ18F020001), 2025 Key Technological Innovation Program of Ningbo City(No.2023Z224)

Corresponding Authors: ZHAO Jieyu, Ph.D., professor. His research interests include image graphics technology, natural interaction, machine learning, and computer vision.

About author:: ZHANG Hantao, Master student. His research interests include computer vision, unsupervised learning, and cluster analysis. YE Xulun, Ph.D., professor. His research interests include Bayesian learning, nonparametric clustering, and convex analysis.

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Cite this article:

ZHANG Hantao,ZHAO Jieyu,YE Xulun. Joint Self-Expressive Subspace Clustering Method for Dynamic Data[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(1): 68-81.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202501005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I1/68

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