FEATURE ELIMINATION IN KERNEL MACHINES IN MODERATELY HIGH DIMENSIONS
| Autor: | Michael R. Kosorok, Yair Goldberg, Sayan Dasgupta |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences 68T05 62G08 business.industry Statistical learning Kernel machines Feature vector Feature selection Pattern recognition Machine Learning (stat.ML) support vector machines Article Support vector machine Feature (computer vision) Statistics - Machine Learning Kernel (statistics) recursive feature elimination Artificial intelligence Statistics Probability and Uncertainty business 62G20 Mathematics variable selection |
| Zdroj: | Ann. Statist. 47, no. 1 (2019), 497-526 |
| Popis: | We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features.We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions.We present four case studies to show that the assumptions are met in most practical situations and present simulation results to demonstrate performance of the proposed approach. 50 pages, 5 figures, submitted to Annals of Statistics |
| Databáze: | OpenAIRE |
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