Zobrazeno 1 - 10
of 359
pro vyhledávání: '"Kong, Lingchen"'
Regularized models have been applied in lots of areas, with high-dimensional data sets being popular. Because tuning parameter decides the theoretical performance and computational efficiency of the regularized models, tuning parameter selection is a
Externí odkaz:
http://arxiv.org/abs/2405.06889
Fused Lasso was proposed to characterize the sparsity of the coefficients and the sparsity of their successive differences for the linear regression. Due to its wide applications, there are many existing algorithms to solve fused Lasso. However, the
Externí odkaz:
http://arxiv.org/abs/2404.10262
Autor:
Shang, Pan, Kong, Lingchen
Matrix form data sets arise in many areas, so there are lots of works about the matrix regression models. One special model of these models is the adaptive nuclear norm regularized trace regression, which has been proven have good statistical perform
Externí odkaz:
http://arxiv.org/abs/2404.07459
Publikováno v:
In Journal of Multivariate Analysis July 2024 202
Publikováno v:
In Giant June 2024 18
Publikováno v:
In Journal of Multivariate Analysis September 2024 203
Publikováno v:
Journal of Computational and Applied Mathematics 2022
Exploring the relationship among multiple sets of data from one same group enables practitioners to make better decisions in medical science and engineering. In this paper, we propose a sparse collaborative learning (SCL) model, an optimization with
Externí odkaz:
http://arxiv.org/abs/2108.06605
Autor:
Zhang, Bei, Yin, Xiaoka, Zhong, Yanhui, Zang, Quansheng, Wang, Zhenzhong, Kong, Lingchen, Zeng, Ziheng, Fu, Shaowei, Fu, Yu
Publikováno v:
In Construction and Building Materials 16 February 2024 416
Publikováno v:
In Transportation Research Part D February 2024 127