Best Subset Solution Path for Linear Dimension Reduction Models using Continuous Optimization

Autor: Liquet, Benoit, Moka, Sarat, Muller, Samuel

The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high dimensional contexts where the number of variables is usually much larger than the number of observations. In this paper, we focus on

Externí odkaz: http://arxiv.org/abs/2403.20007

Sustainable poverty reduction models for the coordinated development of the social economy and environment in China

Autor: Ge, Yong, Hu, Shan, Song, Yongze, Zheng, Hua, Liu, Yansui, Ye, Xinyue, Ma, Ting, Liu, Mengxiao, Zhou, Chenghu

Publikováno v: In Science Bulletin 15 October 2023 68(19):2236-2246

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Best Subset Solution Path for Linear Dimension Reduction Models Using Continuous Optimization.

Autor: Liquet B; School of Mathematical and Physical Sciences, Macquarie University, Sydney, Australia.; Laboratoire de Mathématiques et de leurs Applications, Université de Pau et des Pays de l'Adour, Pau, France., Moka S; School of Mathematical and Physical Sciences, Macquarie University, Sydney, Australia.; School of Mathematics and Statistics, The University of New South Wales, Sydney, Australia., Muller S; School of Mathematical and Physical Sciences, Macquarie University, Sydney, Australia.; School of Mathematics and Statistics, The University of Sydney, Sydney, Australia.

Publikováno v: Biometrical journal. Biometrische Zeitschrift [Biom J] 2025 Feb; Vol. 67 (1), pp. e70015.

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