Generic chaining and the l1-penalty

We address the choice of the tuning parameter $λ$ in $\ell_1$-penalized M-estimation. Our main concern is models which are highly nonlinear, such as the Gaussian mixture model. The number of parameters $p$ is moreover large, possibly larger than the number of observations $n$. The generic chaining technique of Talagrand[2005] is tailored for this problem. It leads to the choice $λ\asymp \sqrt {\log p / n}$, as in the standard Lasso procedure (which concerns the linear model and least squares loss).

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