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Introduction

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Part of the Lecture Notes in Mathematics book series (LNMECOLE,volume 2159)

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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References

  • T. Hastie, R. Tibshirani, M. Wainwright, Statistical Learning with Sparsity: The Lasso and Generalizations (CRC Press, Boca Raton, 2015)

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© 2016 Springer International Publishing Switzerland

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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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