A nonlinear classifier based on factorization machines model

Abstract

Polynomial Classifier (PC) is a powerful nonlinear classification method that has been widely used in many pattern recognition problems. Despite its high classification accuracy, its computational cost for both training and testing is polynomial with the dimensionality of input data, which makes it unsuitable for large-scale problems. In this work, based on the idea of factorization machines (FMs), we propose an efficient classification method which approximates PC by performing a low-rank approximation to the coefficient matrix of PC. Our method can largely preserve the accuracy of PC, while has only linear computational complexity with the data dimensionality. We conduct extensive experiments to show the effectiveness of our method.