Hyperparameter Selection for Group-Sparse Regression: A Probabilistic Approach

TL;DR: This paper presents a novel dimension reduction method termed discriminative sparse embedding (DSE) based on adaptive graph, which learns a sparse weight matrix which can reduce the effects of redundant information and noises of the original data, and uncover essential structural relationship among the data.

TL;DR: The generalized least absolute shrinkage and selection operator (LASSO) is generalized, in order to allow for changes in the spectral content, and the resulting LASSO formulation can be solved in a simple manner using cyclic minimization, enabling recursive estimation for non-stationary signals.

TL;DR: This paper presents an online formulation of the generalized sparse covariance-based estimator (SPICE), which has the advantage of allowing full control over the sparsity in the estimate in a fully data adaptive and noise independent manner.

TL;DR: The problem of time-delays estimation of backscattered echoes from underwater targets is presented using a sparse reconstruction framework employing an integrated dictionary, which may be used to find initial regions of the parameters of interest using a smaller dictionary than otherwise required, without suffering a loss of performance.

TL;DR: It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.

TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.

TL;DR: The Fundamentals of Statistical Signal Processing: Estimation Theory as mentioned in this paper is a seminal work in the field of statistical signal processing, and it has been used extensively in many applications.

TL;DR: The authors prove two results about this type of estimator that are unprecedented in several ways: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures.