A Safe Feature Screening Rule for Rank Lasso

• Published in: IEEE signal processing letters Vol. 29; pp. 1062 - 1066
• Main Authors: Shang, Pan, Kong, Lingchen, Liu, Dashuai
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational efficiency; Computational modeling; Computing costs; Data models; Datasets; Dual circumscribed sphere technique; Mathematical models; Outliers (statistics); Random errors; Rank Lasso; Robustness (mathematics); Screening; screening rule; Task analysis; Transforms; Tuning

To deal with outliers or heavy-tailed random errors in common high-dimensional data sets, robust regressions are preferable selections and Rank Lasso is a notable model among them. However, the large-scaled feature size in data set increases the computational cost of solving Rank Lasso. In this pape...