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Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) doi:10.1016/j.chemolab.2017.07.004. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.
Citation | OHPL citation info |
ohpl.io | |
ohpl.io/doc/ | |
github.com/nanxstats/OHPL | |
Bug report | File report |
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