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Subspace clustering via stacked independent subspace analysis networks with sparse prior information
Pattern Recognition Letters ( IF 5.1 ) Pub Date : 2021-03-27 , DOI: 10.1016/j.patrec.2021.03.026
Zongze Wu , Chunchen Su , Ming Yin , Zhigang Ren , Shengli Xie

Sparse subspace clustering (SSC) method has gained considerable attention in recent decades owing to its advantages in the fields of clustering. In essence, SSC is to learn a sparse affinity matrix followed by striving for a low-dimensional representation of data. However, the SSC and its variants mainly focus on building high-quality affinity matrix while ignoring the importance of low-dimensional feature derived from the affinity matrix. Moreover, due to their intrinsic linearity of models, they cannot efficiently handle data with the nonlinear distribution. In this paper, we propose a stacked independent subspace analysis (ISA) with sparse prior information called stacked-ISASP to deal with these two issues. Powered by handling data with nonlinear structure, our method aims at seeking a low-dimensional feature from the image data. Concretely, the model can stack the modified independent subspace analysis networks by incorporating the prior subspace information from the original data. To validate the efficiency of the proposed method, we compare our proposed stacked-ISASP method with the state-of-the-art methods on real datasets. Experimental results show that our approach can not only learn a better low-dimensional structure from the data but also achieve better performance for the classification task.



中文翻译:

通过具有稀疏先验信息的堆叠式独立子空间分析网络进行子空间聚类

稀疏子空间聚类(SSC)方法由于其在聚类领域中的优势,最近几十年来受到了广泛的关注。本质上,SSC是要学习稀疏的亲和矩阵,然后努力争取数据的低维表示。但是,SSC及其变体主要侧重于构建高质量的亲和度矩阵,而忽略了从亲和度矩阵得出的低维特征的重要性。而且,由于模型的固有线性,它们不能有效地处理具有非线性分布的数据。在本文中,我们提出了一种具有独立先验信息的堆叠式独立子空间分析(ISA),称为堆叠式ISASP,以处理这两个问题。通过处理具有非线性结构的数据,我们的方法旨在从图像数据中寻找低维特征。具体来说,该模型可以通过合并原始数据中的先前子空间信息来堆叠修改后的独立子空间分析网络。为了验证所提出方法的效率,我们将我们提出的堆叠式ISASP方法与实际数据集上的最新方法进行了比较。实验结果表明,我们的方法不仅可以从数据中学习到更好的低维结构,而且可以实现更好的分类任务性能。

更新日期:2021-04-02
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