Identifying a minimal class of models for high-dimensional data

Daniel Nevo, Ya'acov Ritov

Research output: Contribution to journalArticlepeer-review


Model selection consistency in the high{dimensional regression setting can be achieved only if strong assumptions are fulfilled. We therefore suggest to pursue a difierent goal, which we call a minimal class of models. The minimal class of models includes models that are similar in their prediction accuracy but not necessarily in their elements. We suggest a random search algorithm to reveal candidate models. The algorithm implements simulated annealing while using a score for each predictor that we suggest to derive using a combination of the lasso and the elastic net. The utility of using a minimal class of models is demonstrated in the analysis of two data sets.

Original languageEnglish
JournalJournal of Machine Learning Research
StatePublished - 1 Apr 2017
Externally publishedYes


FundersFunder number
Israel Science Foundation1770/15


    • Elastic net
    • High{dimensional data
    • Lasso
    • Model selection
    • Simulated annealing


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