Abstract
When there are more measurements per unit of observation than there are observations, data are called “high-dimensional”. Today’s data are often high-dimensional mainly due to the easy way to record or obtain data using the internet, or cameras, or new biomedical technologies, or shopping cards, etc. High-dimensional data can also be “constructed” from only a few variables by considering for example second, third, and higher order interactions.
Keywords
- Loss Function
- Empirical Risk
- Dual Norm
- Empirical Risk Minimization
- Oracle Inequality
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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T. Hastie, R. Tibshirani, M. Wainwright, Statistical Learning with Sparsity: The Lasso and Generalizations (CRC Press, Boca Raton, 2015)
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van de Geer, S. (2016). Introduction. In: Estimation and Testing Under Sparsity. Lecture Notes in Mathematics(), vol 2159. Springer, Cham. https://doi.org/10.1007/978-3-319-32774-7_1
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DOI: https://doi.org/10.1007/978-3-319-32774-7_1
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