A life-long learning vector quantization approach for interactive learning of multiple categories

Authors:
Stephan Kirstein;Heiko Wersing;Horst-Michael Gross;Edgar Körner
Affiliations:
Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, 63073 Offenbach am Main, Germany and Ilmenau University of Technology, Neuroinformatics and Cognitive Robotics Lab, P.O.B. 100565, 98684 ...;Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, 63073 Offenbach am Main, Germany;Ilmenau University of Technology, Neuroinformatics and Cognitive Robotics Lab, P.O.B. 100565, 98684 Ilmenau, Germany;Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, 63073 Offenbach am Main, Germany
Venue:
Neural Networks
Year:
2012

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Abstract

We present a new method capable of learning multiple categories in an interactive and life-long learning fashion to approach the ''stability-plasticity dilemma''. The problem of incremental learning of multiple categories is still largely unsolved. This is especially true for the domain of cognitive robotics, requiring real-time and interactive learning. To achieve the life-long learning ability for a cognitive system, we propose a new learning vector quantization approach combined with a category-specific feature selection method to allow several metrical ''views'' on the representation space of each individual vector quantization node. These category-specific features are incrementally collected during the learning process, so that a balance between the correction of wrong representations and the stability of acquired knowledge is achieved. We demonstrate our approach for a difficult visual categorization task, where the learning is applied for several complex-shaped objects rotated in depth.