efficient regularized regression with l0 penalty for variable selection and network construction
Clicks: 148
ID: 249595
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
148 views
22 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #123 of 191 articles by views in advanced functional materials
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 191 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Variable selections for regression with high-dimensional big data have found many applications in bioinformatics and computational biology. One appealing approach is the L0 regularized regression which penalizes the number of nonzero features in the model directly. However, it is well known that L0 optimization is NP-hard and computationally challenging. In this paper, we propose efficient EM (L0EM) and dual L0EM (DL0EM) algorithms that directly approximate the L0 optimization problem. While L0EM is efficient with large sample size, DL0EM is efficient with high-dimensional (n≪m) data. They also provide a natural solution to all Lp p∈[0,2] problems, including lasso with p=1 and elastic net with p∈[1,2]. The regularized parameter λ can be determined through cross validation or AIC and BIC. We demonstrate our methods through simulation and high-dimensional genomic data. The results indicate that L0 has better performance than lasso, SCAD, and MC+, and L0 with AIC or BIC has similar performance as computationally intensive cross validation. The proposed algorithms are efficient in identifying the nonzero variables with less bias and constructing biologically important networks with high-dimensional big data.
| Reference Key |
liu2016computationalefficient
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Zhenqiu Liu;Gang Li |
| Journal | advanced functional materials |
| Year | 2016 |
| DOI |
10.1155/2016/3456153
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.