Learning with feature side-information

Mollaysa, Amina (Haute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale) ; Strasser, Pablo (Haute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale) ; Kalousis, Alexandros (Haute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale)

Very often features come with their own vectorial descriptions which provide detailed information about their properties. We refer to these vectorial descriptions as feature side-information. The feature side-information is most often ignored or used for feature selction prior to model fitting. In this paper, we propose a framework that allows for the incorporation of feature side-information during the learning of very general model families. We control the structures of the learned models so that they reflect features’ similarities as these are defined on the basis of the side-information. We perform experiments on a number of benchmark datasets which show significant predictive performance gains, over a number of baselines, as a result of the exploitation of the side-information.


Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG - Genève
Institute:
CRAG - Centre de Recherche Appliquée en Gestion
Subject(s):
Economie/gestion
Publisher:
Barcelona, Spain, 5-10 December 2016
Date:
Barcelona, Spain
5-10 December 2016
2016
Pagination:
5 p.
Published in:
Learning in High Dimensions with Structure, Workshop proceedings of the 30th Conference on Neural Information Processing Systems (NIPS 2016)
External resources:
Appears in Collection:



 Record created 2017-10-18, last modified 2019-06-11

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