Supervised Dictionary Learning and Sparse Representation-A Review (original) (raw)

Dictionary learning and sparse representation (DLSR) is a recent and successful mathematical model for data representation that achieves state-ofthe-art performance in various fields such as pattern recognition, machine learning, computer vision, and medical imaging. The original formulation for DLSR is based on the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although this formulation is optimal for solving problems such as denoising, inpainting, and coding, it may not lead to optimal solution in classification tasks, where the ultimate goal is to make the learned dictio- * Corresponding author Email addresses: mehrdad.gangeh@utoronto.ca (Mehrdad J. Gangeh), afarahat@pami.uwaterloo.ca (Ahmed K. Farahat), aghodsib@uwaterloo.ca (Ali Ghodsi), mkamel@pami.uwaterloo.ca (Mohamed S. Kamel)