多项式网络和因式分解机：新的见解和有效的训练算法
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
摘要：
多项式网络和因子分解机是最近提出的两种模型，可以有效地在分类和回归任务中使用特征交互。在本文中，我们从一个统一的角度重新审视这两种模式。基于这一新的观点，我们研究了这两种模型的性质，并提出了新的有效的训练算法。我们的方法的关键是将参数学习作为一个低秩对称张量估计问题，我们通过多凸优化来解决。我们展示了我们关于回归和推荐系统任务的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
