Class-Specific sparse coding for learning of object representations

Authors:
Stephan Hasler;Heiko Wersing;Edgar Körner
Affiliations:
Honda Research Institute Europe GmbH, Offenbach am Main, Germany;Honda Research Institute Europe GmbH, Offenbach am Main, Germany;Honda Research Institute Europe GmbH, Offenbach am Main, Germany
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Year:
2005

Citing 3
Cited 1

Learning optimized features for hierarchical models of invariant object recognition

Neural Computation
Non-negative Matrix Factorization with Sparseness Constraints

The Journal of Machine Learning Research
Recognition invariance obtained by extended and invariant features

Neural Networks - 2004 Special issue Vision and brain

A comparison of features in parts-based object recognition hierarchies

ICANN'07 Proceedings of the 17th international conference on Artificial neural networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present two new methods which extend the traditional sparse coding approach with supervised components. The goal of these extensions is to increase the suitability of the learned features for classification tasks while keeping most of their general representation performance. A special visualization is introduced which allows to show the principal effect of the new methods. Furthermore some first experimental results are obtained for the COIL-100 database.