This paper develops a novel iterative framework for subspace clustering (SC) in a learned discriminative feature domain. This framework consists of two modules of fuzzy sparse SC and discriminative transformation learning. In the first module, fuzzy latent labels containing discriminative information and latent representations capturing the subspace structure will be simultaneously evaluated in a feature domain. Then the linear transforming operator with respect to the feature domain will be successively updated in the second module with the advantages of more discrimination, subspace structure preservation, and robustness to outliers. These two modules will be alternatively carried out and both theoretical analysis and empirical evaluations will demonstrate its effectiveness and superiorities. In particular, experimental results on three benchmark databases for SC clearly illustrate that the proposed framework can achieve significant improvements than other state-of-the-art approaches in terms of clustering accuracy.

中文翻译：

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模糊稀疏子空间聚类的判别变换学习


本文为学习的判别特征域中的子空间聚类（SC）开发了一种新颖的迭代框架。该框架由模糊稀疏SC和判别变换学习两个模块组成。在第一个模块中，包含判别信息的模糊潜在标签和捕获子空间结构的潜在表示将在特征域中同时评估。然后，在第二个模块中将陆续更新针对特征域的线性变换算子，其优点是更具区分性、子空间结构保留和对异常值的鲁棒性。这两个模块将交替进行，理论分析和实证评价都将证明其有效性和优越性。特别是，在 SC 的三个基准数据库上的实验结果清楚地表明，所提出的框架在聚类精度方面比其他最先进的方法可以实现显着的改进。