3 accepted papers
Recent years have witnessed an explosion of Multi- view Subspace Classification (MSCla) and Multi-view Subspace Clustering (MSClu) methods for various applications. However, their theoretical foundation have not been well explored and understood. In this paper, we investigate the multi-view subspace
The deep autoencoder network which is based on constraining non-negative weights, can learn a low dimensional part-based representation. On the other hand, the inherent structure of the each data cluster can be described by the distribution of the intraclass sample. Then one hopes to learn a new low…
Most of the existing subspace clustering (SC) frameworks assume that the noise contaminating the data is generated by an independent and identically distributed (i.i.d.) source, where the Gaussianity is often imposed. Though these assumptions greatly simplify the underlying problems, they do not hol…