Sparsity aware consistent and high precision variable selection
Variable selection is fundamental while dealing with sparse signals that contain only a few number of nonzero elements. This is the case in many signal processing areas extending from high-dimensional statistical modeling to sparse signal estimation. This paper explores a new and efficient approach to model a system with underlying sparse parameters. The idea is to get the noisy observations and estimate the minimum number of underlying parameters with acceptable estimation accuracy. The main challenge is due to the non-convex optimization problem to be solved. The reconstruction stage deals with some suitable objective function in order to estimate the original sparse signal by performing variable selection procedure. This paper introduces a suitable objective function in order to simultaneously recover the true support of the underlying sparse signal while still achieving an acceptable estimation error. It is shown that the proposed method performs the best variable selection compared to the other algorithms, while approaching the lowest least mean squared error in almost all the cases.
variable selection estimation sparse signal reconstruction lasso