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

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...

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Bibliographische Detailangaben
Veröffentlicht in:	Annals of mathematics and artificial intelligence Jg. 90; H. 7-9; S. 809 - 829
Hauptverfasser:	Le Thi, Hoai An, Ho, Vinh Thanh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.09.2022 Springer Springer Nature B.V Springer Verlag
Schlagworte:	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
ISSN:	1012-2443, 1573-7470
Online-Zugang:	Volltext
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