Supervised Dictionary Learning
Julien Mairal, Francis Bach, Jean Ponce, Andrew Zisserman and Guillermo Sapiro
In: NIPS 2008(2009).
It is now well established that sparse signal models are well suited for restoration
tasks and can be effectively learned from audio, image, and video data. Recent
research has been aimed at learning discriminative sparse models instead of
purely reconstructive ones. This paper proposes a new step in that direction, with
a novel sparse representation for signals belonging to different classes in terms of
a shared dictionary and discriminative class models. The linear version of the proposed
model admits a simple probabilistic interpretation, while its most general
variant admits an interpretation in terms of kernels. An optimization framework
for learning all the components of the proposed model is presented, along with
experimental results on standard handwritten digit and texture classification tasks.