Abstract
Subspace clustering is a useful technique for many
computer vision applications in which the intrinsic
dimension of high-dimensional data is smaller than
the ambient dimension. Traditional subspace clustering methods often rely on the self-expressiveness
property, which has proven effective for linear subspace clustering. However, they perform unsatisfactorily on real data with complex nonlinear subspaces. More recently, deep autoencoder based
subspace clustering methods have achieved success owning to the more powerful representation
extracted by the autoencoder network. Unfortunately, these methods only considering the reconstruction of original input data can hardly guarantee the latent representation for the data distributed in subspaces, which inevitably limits the performance in practice. In this paper, we propose a
novel deep subspace clustering method based on
a latent distribution-preserving autoencoder, which
introduces a distribution consistency loss to guide
the learning of distribution-preserving latent representation, and consequently enables strong capacity
of characterizing the real-world data for subspace
clustering. Experimental results on several public
databases show that our method achieves signifi-
cant improvement compared with the state-of-theart subspace clustering methods