  Reconstruction based subspace clustering methods compute a self
reconstruction matrix over the samples and use it for spectral clustering to
obtain the final clustering result. Their success largely relies on the
assumption that the underlying subspaces are independent, which, however, does
not always hold in the applications with increasing number of subspaces. In
this paper, we propose a novel reconstruction based subspace clustering model
without making the subspace independence assumption. In our model, certain
properties of the reconstruction matrix are explicitly characterized using the
latent cluster indicators, and the affinity matrix used for spectral clustering
can be directly built from the posterior of the latent cluster indicators
instead of the reconstruction matrix. Experimental results on both synthetic
and real-world datasets show that the proposed model can outperform the
state-of-the-art methods.
