Online Hyperparameter-Free Sparse Estimation Method

In this paper, we derive an online estimator for sparse parameter vectors which, unlike the LASSO approach, does not require the tuning of any hyperparameters. The algorithm is based on a covariance matching approach and is equivalent to a weighted version of the square-root LASSO. The computational complexity of the estimator is of the same order as that of the online versions of regularized least-squares (RLS) and LASSO. We provide a numerical comparison with feasible and infeasible implementations of the LASSO and RLS to illustrate the advantage of the proposed online hyperparameter-free estimator.

• Publication year

  2015

• Venue

  IEEE Transactions on Signal Processing

• Publication date

  2015-05-06

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/TSP.2015.2421472arXiv 1505.01461
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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