Lasso-type variable selection has been increasingly adopted in many applications. In this paper, we propose a covariate-correlated Lasso that selects the covariates correlated more strongly with the response variable. We propose an efficient algorithm to solve this Lasso-type optimization and prove its convergence. Experiments on DNA gene expression data sets show that the selected covariates correlate more strongly with the response variable, and the residual values are decreased, indicating better covariate selection. The selected covariates lead to better classification performance. © 2014 Springer-Verlag.
CITATION STYLE
Jiang, B., Ding, C., & Luo, B. (2014). Covariate-correlated Lasso for feature selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8724 LNAI, pp. 595–606). Springer Verlag. https://doi.org/10.1007/978-3-662-44848-9_38
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