A vector quantization approach for life-long learning of categories

Authors:
Stephan Kirstein;Heiko Wersing;Horst-Michael Gross;Edgar Körner
Affiliations:
Ilmenau University of Technology, Neuroinformatics and Cognitive Robotics Lab, Ilmenau, Germany and Honda Research Institute Europe GmbH, Offenbach am Main, Germany;Honda Research Institute Europe GmbH, Offenbach am Main, Germany;Ilmenau University of Technology, Neuroinformatics and Cognitive Robotics Lab, Ilmenau, Germany;Honda Research Institute Europe GmbH, Offenbach am Main, Germany
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

We present a category learning vector quantization (cLVQ) approach for incremental and life-long learning of multiple visual categories where we focus on approaching the stability-plasticity dilemma. To achieve the life-long learning ability an incremental learning vector quantization approach is combined with a category-specific feature selection method in a novel way to allow several metrical "views" on the representation space for the same cLVQ nodes.