中国科学技术大学学报 ›› 2019, Vol. 49 ›› Issue (2): 159-165.DOI: 10.3969/j.issn.0253-2778.2019.02.011

• 原创论文 • 上一篇    下一篇

基于辅助信息的混合线性矩阵补全模型

宋 辉   

  1. 南京师范大学计算机科学与技术学院,江苏南京 210046
  • 收稿日期:2018-09-22 修回日期:2018-12-04 出版日期:2019-02-28 发布日期:2019-02-28
  • 通讯作者: 杨明
  • 作者简介:宋辉,男,1994年生,硕士生,研究方向:机器学习、推荐系统.E-mail: songhui94@163.com.
  • 基金资助:
    国家自然科学基金(61876087,61272222),国家自然科学基金重点项目(61432008)资助.

Mixed linear matrix completion model based on auxiliary information

SONG Hui   

  1. Department of Computer Science and Technology, Nanjing Normal University,Nanjing,Jiangsu,210046
  • Received:2018-09-22 Revised:2018-12-04 Online:2019-02-28 Published:2019-02-28

摘要: 矩阵补全技术在近年来已经在诸多领域得到了应用,为此提出一种将双线性关系与单边线性关系混合的矩阵补全模型,同时关注行信息与列信息之间的相关性和他们各自的特点,使得混合线性模型能够尽可能地逼近原始观测矩阵.此外还证明了使用ADMM算法求解的收敛性,并通过拟合数据和真实数据两组实验证明了同其他使用辅助信息的补全模型相比,该方法获得补全结果在RMSE评价标准下的误差相对降低了25%以上.

关键词: 矩阵补全, 辅助特征信息, 混合线性, 行列相关性, ADMM算法

Abstract: The matrix completion technology has been applied in many fields in recent years. A matrix completion model that mixes bilinear and unilateral linear relationship is proposed, considering the correlation between row information and column information and their respective characteristics, so that the mixed linear model can approximate the original matrix entries. The convergence of using the ADMM algorithm to solve the convex optimization problem is proved, and makes two sets of experiments with synthetic datasets and real datasets, which proves that the proposed method is more effective compared with the existing model using auxiliary information, whose error under RMSE evaluation standard has been reduced by more than 25%.

Key words: matrix completion, auxiliary feature information, mixed linear, row and column correlation, ADMM method