Mollaysa, Amina; Strasser, Pablo; Kalousis, Alexandros Regularising Non-linear Models Using Feature Side-information Inproceedings Proceedings of the 34th International Conference on Machine Learning,
ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, pp. 2508–2517, 2017. Abstract | Links | BibTeX @inproceedings{DBLP:conf/icml/MollaysaSK17,
title = {Regularising Non-linear Models Using Feature Side-information},
author = {Amina Mollaysa and Pablo Strasser and Alexandros Kalousis},
url = {http://proceedings.mlr.press/v70/mollaysa17a/mollaysa17a.pdf
http://proceedings.mlr.press/v70/mollaysa17a/mollaysa17a-supp.pdf
https://bitbucket.org/dmmlgeneva/code_icml2017_regularising-non-linear-models-using-feature},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the 34th International Conference on Machine Learning,
ICML 2017, Sydney, NSW, Australia, 6-11 August 2017},
pages = {2508--2517},
crossref = {DBLP:conf/icml/2017},
abstract = {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. In the standard learning scenario, input is represented as a vector of features and the feature side-information is most often ignored or used only for feature selection prior to model fitting. We believe that feature side-information which carries information about features intrinsic property will help improve model prediction if used in a proper way during learning process. In this paper, we propose a framework that allows for the incorporation of the feature side-information during the learning of very general model families to improve the prediction performance. 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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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. In the standard learning scenario, input is represented as a vector of features and the feature side-information is most often ignored or used only for feature selection prior to model fitting. We believe that feature side-information which carries information about features intrinsic property will help improve model prediction if used in a proper way during learning process. In this paper, we propose a framework that allows for the incorporation of the feature side-information during the learning of very general model families to improve the prediction performance. 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. |
