统计与决策2024，Vol.40Issue(6)：39-44,6.DOI:10.13546/j.cnki.tjyjc.2024.06.007
矩阵数据的分类预测方法
Classification Prediction Methods for Matrix Data
摘要
Abstract
This paper studies the parameter estimation and classification method of matrix data under the matrix normal dis-tribution.Firstly,based on the low-rank decomposition and the penalized likelihood function method of matrix normal distribution,a method for parameter estimation and adaptive determination of rank of matrix data is proposed.Then the block coordinate de-scent method and the augmented Lagrange multiplier algorithm are used to give an effective iterative estimation algorithm.Fur-thermore,based on the discriminant analysis method,the rule of classi fication and prediction under low-rank decomposition is proposed.Finally,through the application of a large number of numerical simulations and the recognition of satellite land resource data and handwritten digits,the low-rank estimation method is proved to be effective in improving the estimation and classification prediction accuracy of matrix data.关键词
矩阵正态分布/低秩分解/判别分析/分类预测Key words
matrix normal distribution/low-rank decomposition/discriminant analysis/classification prediction分类
数理科学引用本文复制引用
汪钱荣,陈文钰,赵为华..矩阵数据的分类预测方法[J].统计与决策,2024,40(6):39-44,6.基金项目
国家社会科学基金资助项目(22BTJ025) （22BTJ025）
国家级大学生创新实践项目(202210304005Z) （202210304005Z）
