Alternating DCA for reduced-rank multitask linear regression with covariance matrix estimation

• Published in: Annals of mathematics and artificial intelligence Vol. 90; no. 7-9; pp. 809 - 829
• Main Authors: Le Thi, Hoai An, Ho, Vinh Thanh
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2022; Springer; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Artificial Intelligence; Complex Systems; Computer Science; Convexity; Covariance matrix; Critical point; Machine learning; Mathematics; Regression analysis; Regression models; DCA; 62J05; Reduced-rank multitask linear regression; DC programming; 90C90; 90C26; Partial DC program; Covariance matrix estimation; Alternating DCA; Partial DC programming

We study a challenging problem in machine learning that is the reduced-rank multitask linear regression with covariance matrix estimation. The objective is to build a linear relationship between multiple output variables and input variables of a multitask learning process, taking into account the ge...