Multi-task learning via non-sparse multiple kernel learning

Authors:
Wojciech Samek;Alexander Binder;Motoaki Kawanabe
Affiliations:
Technical University of Berlin, Berlin, Germany and Fraunhofer Institute FIRST, Berlin, Germany;Technical University of Berlin, Berlin, Germany;Fraunhofer Institute FIRST, Berlin, Germany and Technical University of Berlin, Berlin, Germany
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Year:
2011

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Abstract

In object classification tasks from digital photographs, multiple categories are considered for annotation. Some of these visual concepts may have semantic relations and can appear simultaneously in images. Although taxonomical relations and co-occurrence structures between object categories have been studied, it is not easy to use such information to enhance performance of object classification. In this paper, we propose a novel multi-task learning procedure which extracts useful information fromthe classifiers for the other categories. Our approach is based on non-sparse multiple kernel learning (MKL) which has been successfully applied to adaptive feature selection for image classification. Experimental results on PASCAL VOC 2009 data show the potential of our method.