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Alexander Novikov, Mikhail Trofimov, Ivan Oseledets
Nov 04, 2016 (modified: Jan 17, 2017)ICLR 2017 conference submissionreaders: everyone
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.
TL;DR:A supervised machine learning algorithm with a polynomial decision function (like SVM with a polynomial kernel) that models exponentially many polynomial terms by factorizing the tensor of the parameters.