Sparsity and the Truncated $l^2$-norm


Lee Dicker ;
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:159-166, 2014.


Sparsity is a fundamental topic in high-dimensional data analysis. Perhaps the most common measures of sparsity are the $l^p$-norms, for $p < 2$. In this paper, we study an alternative measure of sparsity, the truncated $l^2$-norm, which is related to other $l^p$-norms, but appears to have some unique and useful properties. Focusing on the n-dimensional Gaussian location model, we derive exact asymptotic minimax results for estimation over truncated $l^2$-balls, which complement existing results for $l^p$-balls. We then propose simple new adaptive thresholding estimators that are inspired by the truncated $l^2$-norm and are adaptive asymptotic minimax over $l^p$-balls ($p < 2$), as well as truncated $l^2$-balls. Finally, we derive lower bounds on the Bayes risk of an estimator, in terms of the parameter’s truncated $l^2$-norm. These bounds provide necessary conditions for Bayes risk consistency in certain problems that are relevant for high-dimensional Bayesian modeling.

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