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.
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2025, Vol. 38 
