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论文类型：期刊论文

发表时间：2019-11-01

发表刊物：NEURAL COMPUTING & APPLICATIONS

收录刊物：SCIE

卷号：31

期号：11

页面范围：7921-7933

ISSN号：0941-0643

关键字：Subspace learning; Low-rank representation; Manifold clustering; Spectral clustering-based methods

摘要：Spectral clustering-based subspace clustering methods have attracted broad interest in recent years. This kind of methods usually uses the self-representation in the original space to extract the affinity between the data points. However, we can usually find a subspace where the affinity of the projected data points can be extracted by self-representation more effectively. Moreover, only using the self-representation in the original space cannot handle nonlinear manifold clustering well. In this paper, we present robust subspace learning-based low-rank representation learning a subspace favoring the affinity extraction for the low-rank representation. The process of learning the subspace and yielding the representation is conducted simultaneously, and thus, they can benefit from each other. After extending the linear projection to nonlinear mapping, our method can handle manifold clustering problem which can be viewed as a general case of subspace clustering. In addition, the l2,1-norm used in our model can increase the robustness of our method. Extensive experimental results demonstrate the effectiveness of our method on manifold clustering.