Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
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.
Cite
Text
Blondel et al. "Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms." International Conference on Machine Learning, 2016.Markdown
[Blondel et al. "Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms." International Conference on Machine Learning, 2016.](https://mlanthology.org/icml/2016/blondel2016icml-polynomial/)BibTeX
@inproceedings{blondel2016icml-polynomial,
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 = {International Conference on Machine Learning},
year = {2016},
pages = {850-858},
volume = {48},
url = {https://mlanthology.org/icml/2016/blondel2016icml-polynomial/}
}