  This paper develops a novel iterative framework for subspace clustering in a
learned discriminative feature domain. This framework consists of two modules
of fuzzy sparse subspace clustering and discriminative transformation learning.
In the first module, fuzzy latent labels containing discriminative information
and latent representations capturing the subspace structure will be
simultaneously evaluated in a feature domain. Then the linear transforming
operator with respect to the feature domain will be successively updated in the
second module with the advantages of more discrimination, subspace structure
preservation and robustness to outliers. These two modules will be
alternatively carried out and both theoretical analysis and empirical
evaluations will demonstrate its effectiveness and superiorities. In
particular, experimental results on three benchmark databases for subspace
clustering clearly illustrate that the proposed framework can achieve
significant improvements than other state-of-the-art approaches in terms of
clustering accuracy.
