【摘要】 自表示子空间聚类方法在高维数据处理中表现优秀，已成为该领域的关键技术之一.然而，传统的自表示模型通常假设数据集是静态的，难以适应动态、连续到达的数据流，会导致新旧数据存在特征异构、新到样本可能包含未知新类别等情况.因此，文中提出联合自表示子空间聚类方法(Joint Self-Expressive Subspace Clustering Method, JSSC),可适应数据流的连续到达.JSSC结合联合自表示特征学习模块和新类别样本处理模块，有效聚类新类别样本，同时确保已有类别的聚类性能不受影响.此外，该方法利用深度自动编码器学习子空间基，实现直观、可解释的表示，并通过成对目标和正则化项，同时管理已知类别和新兴类别.基准数据集上的实验表明，JSSC在聚类任务中表现较优，尤其是在处理动态数据中的新类别方面.更多还原

【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.更多还原