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(Neural Computation. 2007;19:1897-1918.)
© 2007 The MIT Press

Letter

Combining Reconstruction and Discrimination with Class-Specific Sparse Coding

Stephan Hasler

Stephan.hasler{at}honda-ri.de Honda Research Institute Europe GmbH, 63073 Offenbach/Main, Germany

Heiko Wersing

Heiko.wersing{at}honda-ri.de Honda Research Institute Europe GmbH, 63073 Offenbach/Main, Germany

Edgar Körner

Edgar.koerner{at}honda-ri.de Honda Research Institute Europe GmbH, 63073 Offenbach/Main, Germany

Sparse coding is an important approach for the unsupervised learning of sensory features. In this contribution, we present two new methods that extend the traditional sparse coding approach with supervised components. Our goal is to increase the suitability of the learned features for classification tasks while keeping most of their general representation capability. We analyze the effect of the new methods using visualization on artificial data and discuss the results on two object test sets with regard to the properties of the found feature representation.






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Copyright © 2007 by The MIT Press.