An Efficient Algorithm for Solving the Matrix Optimization Problem in the Unsupervised Feature Selection

被引：0
|
作者：
Li, Chunmei [1 ]
Wu, Wen [1 ]
机构：
[1] Guilin Univ Elect Technol, Guangxi Coll & Univ Key Lab Data Anal & Computat, Sch Math & Computat Sci, Guilin 541004, Peoples R China
来源：
SYMMETRY-BASEL | 2022年 / 14卷 / 03期
基金：
中国国家自然科学基金;
关键词：
symmetric matrix optimization problem; numerical method; convergence analysis; unsupervised feature selection; FACTORIZATION;
D O I：
10.3390/sym14030462
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
In this paper, we consider the symmetric matrix optimization problem arising in the process of unsupervised feature selection. By relaxing the orthogonal constraint, this problem is transformed into a constrained symmetric nonnegative matrix optimization problem, and an efficient algorithm is designed to solve it. The convergence theorem of the new algorithm is derived. Finally, some numerical examples show that the new method is feasible. Notably, some simulation experiments in unsupervised feature selection illustrate that our algorithm is more effective than the existing algorithms.
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页数：16
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