Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
This work addresses the need for efficient training in models that use feature interactions, such as for classification and regression in machine learning applications, but it is incremental as it builds on existing models.
The authors tackled the problem of efficiently training polynomial networks and factorization machines by proposing a unified perspective and new training algorithms based on low-rank symmetric tensor estimation solved via multi-convex optimization, demonstrating results on regression and recommender system tasks.
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.