  This paper presents a novel clustering concept that is based on jointly
learned nonlinear transforms (NTs) with priors on the information loss and the
discrimination. We introduce a clustering principle that is based on evaluation
of a parametric min-max measure for the discriminative prior. The decomposition
of the prior measure allows to break down the assignment into two steps. In the
first step, we apply NTs to a data point in order to produce candidate NT
representations. In the second step, we preform the actual assignment by
evaluating the parametric measure over the candidate NT representations.
Numerical experiments on image clustering task validate the potential of the
proposed approach. The evaluation shows advantages in comparison to the
state-of-the-art clustering methods.
