Sparse Coding with Unity Range Codes and Label Consistent Discriminative Dictionary Learning

A novel sparse coding framework with unity range codes and the possibility to produce a discriminative dictionary is presented. The framework is, in contrast to many other works, able to handle unsupervised, supervised and semi-supervised settings. Furthermore, codes are constrained to be in unity range, which is beneficial in many scenarios. The paper presents the framework and solvers used to produce dictionaries and codes. Experiments in image reconstruction and feature learning for classification highlight the benefits with the proposed framework.