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SDL: Supervised Dictionary Learning
Julien Mairal · Francis Bach · Jean A Ponce · Guillermo Sapiro · Andrew Zisserman

Mon Dec 08 08:45 PM -- 12:00 AM (PST) @

It is now well established that sparse signal models are well suited to restoration tasks and can effectively be 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 multiple decision functions. It is shown that the linear variant of the model admits a simple probabilistic interpretation, and that its most general variant also admits a simple interpretation in terms of kernels. An optimization framework for learning all the components of the proposed model is presented, along with experiments on standard handwritten digit and texture classification tasks.

Author Information

Julien Mairal (Inria)
Francis Bach (INRIA - Ecole Normale Superieure)
Jean A Ponce (University of Illinois at Urbana-Champaign)
Guillermo Sapiro (Duke University)
Andrew Zisserman (DeepMind & University of Oxford)

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