Fast Algorithms for LS and LAD-Collaborative Regression

Jun Sun, Lingchen Kong () and Mei Li ()
Additional contact information
Jun Sun: School of Science, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, P. R. China
Lingchen Kong: School of Science, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, P. R. China
Mei Li: School of Science, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2022, vol. 39, issue 06, 1-29

Abstract: With the development of modern science and technology, it is easy to obtain a large number of high-dimensional datasets, which are related but different. Classical unimodel analysis is less likely to capture potential links between the different datasets. Recently, a collaborative regression model based on least square (LS) method for this problem has been proposed. In this paper, we propose a robust collaborative regression based on the least absolute deviation (LAD). We give the statistical interpretation of the LS-collaborative regression and LAD-collaborative regression. Then we design an efficient symmetric Gauss–Seidel-based alternating direction method of multipliers algorithm to solve the two models, which has the global convergence and the Q-linear rate of convergence. Finally we report numerical experiments to illustrate the efficiency of the proposed methods.

Keywords: Collaborative regression; least absolute deviation; statistical interpretation; duality theory; symmetric Gauss–Seidel; alternating direction method of multipliers (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0217595922500014
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:apjorx:v:39:y:2022:i:06:n:s0217595922500014

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0217595922500014

Access Statistics for this article

Asia-Pacific Journal of Operational Research (APJOR) is currently edited by Gongyun Zhao

More articles in Asia-Pacific Journal of Operational Research (APJOR) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().