Feature splitting parallel algorithm for Dantzig selectors

The Dantzig selector is a widely used and effective method for variable selection in ultra-high-dimensional data. Feature splitting is an efficient processing technique that involves dividing these ultra-high-dimensional variable datasets into manageable subsets that can be stored and processed more...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Statistics and computing Ročník 35; číslo 5
Hlavní autori: Wu, Xiaofei, Chao, Yue, Liang, Rongmei, Tang, Shi, Zhang, Zhimin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.10.2025
Springer Nature B.V
Predmet:
Algorithms
Artificial Intelligence
Computer Science
Convergence
Original Paper
Probability and Statistics in Computer Science
Selectors
Splitting
Statistical Theory and Methods
Statistics and Computing/Statistics Programs
Partition-insensitive
Parallel computing
Proximal point algorithm
Dantzig selector
Feature splitting
ISSN:0960-3174, 1573-1375
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The Dantzig selector is a widely used and effective method for variable selection in ultra-high-dimensional data. Feature splitting is an efficient processing technique that involves dividing these ultra-high-dimensional variable datasets into manageable subsets that can be stored and processed more easily on a single machine. This paper proposes a variable splitting parallel algorithm for solving both convex and nonconvex Dantzig selectors based on the proximal point algorithm. The primary advantage of our parallel algorithm, compared to existing parallel approaches, is the significantly reduced number of iteration variables, which greatly enhances computational efficiency and accelerates the convergence speed of the algorithm. Furthermore, we show that our solution remains unchanged regardless of how the data is partitioned, a property referred to as partition-insensitive. In theory, we use a concise proof framework to demonstrate that the algorithm exhibits linear convergence. Numerical experiments indicate that our algorithm performs competitively in both parallel and nonparallel environments. The R package for implementing the proposed algorithm can be obtained at https://github.com/xfwu1016/PPADS .
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-025-10658-y