ICML 2016poster98 citations
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda
Abstract
Polynomial networks and factorization machines are two recently-proposed models that can efficiently use feature interactions in classification and regression tasks. In this paper, we revisit both models from a unified perspective. Based on this new view, we study the properties of both models and propose new efficient training algorithms. Key to our approach is to cast parameter learning as a low-rank symmetric tensor estimation problem, which we solve by multi-convex optimization. We demonstrate our approach on regression and recommender system tasks.
BibTeX
@InProceedings{pmlr-v48-blondel16,
title = {Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms},
author = {Blondel, Mathieu and Ishihata, Masakazu and Fujino, Akinori and Ueda, Naonori},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {850--858},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/blondel16.pdf},
url = {https://proceedings.mlr.press/v48/blondel16.html},
abstract = {Polynomial networks and factorization machines are two recently-proposed models that can efficiently use feature interactions in classification and regression tasks. In this paper, we revisit both models from a unified perspective. Based on this new view, we study the properties of both models and propose new efficient training algorithms. Key to our approach is to cast parameter learning as a low-rank symmetric tensor estimation problem, which we solve by multi-convex optimization. We demonstrate our approach on regression and recommender system tasks.}
}