Learning Discrimination Specific, Self-Collaborative and Nonlinear Model

This paper presents a novel nonlinear transform model for learning of collaboration structured, discriminative and sparse representations. The idea is to model a collaboration corrective functionality between multiple nonlinear transforms in order to reduce the uncertainty in the estimate. The focus is on the joint estimation of data-adaptive nonlinear transforms (NTs) that take into account a collaboration component w.r.t. a discrimination target. The joint model includes minimum information loss, collaboration corrective and discriminative priors. The model parameters are learned by minimizing an approximation to the empirical negative log likelihood of the model, where we propose an efficient solution by an iterative, coordinate descent algorithm. Numerical experiments validate the potential of the learning principle. The preliminary results show advantages in comparison to the stateof-the-art methods, w.r.t. the learning time, the discriminative quality and the recognition accuracy.