期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 168, 期 -, 页码 62-71出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2017.07.004
关键词
Lasso; Homogeneity pursuit; Sparse learning; Variable ordering; Grouping effect; Partial least squares
类别
资金
- National Natural Science Foundation of China [11271374, 11561010]
- Key Laboratory for Mixed and Missing Data Statistics of the Education Department of Guangxi Province [GXMMSL201404]
- Mathematics and Interdisciplinary Sciences Project
- Central South University
In high-dimensional data modeling, variable selection methods have been a popular choice to improve the prediction accuracy by effectively selecting the subset of informative variables, and such methods can enhance the model interpretability with sparse representation. In this study, we propose a novel group variable selection method named ordered homogeneity pursuit lasso (OHPL) that takes the homogeneity structure in high dimensional data into account. OHPL is particularly useful in high-dimensional datasets with strongly correlated variables. We illustrate the approach using three real-world spectroscopic datasets and compare it with four state-of-the-art variable selection methods. The benchmark results on real-world data show that the proposed method is capable of identifying a small number of influential groups and has better prediction performance than its competitors. The OHPL method and the spectroscopic datasets are implemented and included in an R package OHPL available from https://ohpl.io.
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