Exponential Machines
Abstract
Modeling interactions between features improves the performance of machine learning solutions in many domains (e.g. recommender systems or sentiment analysis). In this paper, we introduce Exponential Machines (ExM), a predictor that models all interactions of every order. The key idea is to represent an exponentially large tensor of parameters in a factorized format called Tensor Train (TT). The Tensor Train format regularizes the model and lets you control the number of underlying parameters. To train the model, we develop a stochastic Riemannian optimization procedure, which allows us to fit tensors with 2^160 entries. We show that the model achieves state-of-the-art performance on synthetic data with high-order interactions and that it works on par with high-order factorization machines on a recommender system dataset MovieLens 100K.
Cite
@article{arxiv.1605.03795,
title = {Exponential Machines},
author = {Alexander Novikov and Mikhail Trofimov and Ivan Oseledets},
journal= {arXiv preprint arXiv:1605.03795},
year = {2017}
}
Comments
ICLR-2017 workshop track paper