Subspace clustering via stacked independent subspace analysis networks with sparse prior information

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.