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.
@article{arxiv.1607.08810,
title = {Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms},
author = {Mathieu Blondel and Masakazu Ishihata and Akinori Fujino and Naonori Ueda},
journal= {arXiv preprint arXiv:1607.08810},
year = {2016}
}