A Nonlinear Classifier Based on Factorization Machines Model

摘要：

　　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.

关键词： Polynomial Classifier Factorization Machines model Low-rank Approximation

作者: Xiaolong Liu Yanming Zhang Chenglin Liu

作者单位: National Laboratory of Pattern Recognition (NLPR)Institute of Automatic,Chinese Academy of Science No. 95,Zhongguancun East Road,Beijing,100190,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 1-10

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）