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Recovery of Sparse Probability Measures via Convex Programming
We propose a direct relaxation of the minimum cardinality problem and show that it can be efficiently solved using convex programming.
Large-Scale Sparse Principal Component Analysis with Application to Text Data
In this paper, we demonstrate the surprising fact that sparse PCA can be easier than PCA in practice, and that it can be reliably applied to very large data sets.
An Homotopy Algorithm for the Lasso with Online Observations
It has been shown that the problem of $\ell_1$-penalized least-square regression commonly referred to as the Lasso or Basis Pursuit DeNoising leads to solutions that are sparse and therefore achieves model selection.
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