Model selection with distributed SCAD penalty

ABSTRACT

In this paper, we focus on the feature extraction and variable selection of massive data which is divided and stored in different linked computers. Specifically, we study the distributed model selection with the Smoothly Clipped Absolute Deviation (SCAD) penalty. Based on the Alternating Direction Method of Multipliers (ADMM) algorithm, we propose distributed SCAD algorithm and prove its convergence. The results of variable selection of the distributed approach are same with the results of the non-distributed approach. Numerical studies show that our method is both effective and efficient which performs well in distributed data analysis.

KEYWORDS:

• Distributed
• sparse
• nonconvex
• SCAD
• ADMM algorithm

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was partially supported by National Natural Science Foundation of China under grant number 11571011.