Reconstructing Sparse Signals via Greedy Monte-Carlo Search

We propose a Monte-Carlo-based method for reconstructing sparse signals in the formulation of sparse linear regression in a high-dimensional setting. The basic idea of this algorithm is to explicitly select variables or covariates to represent a given data vector or responses and accept randomly gen...

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Veröffentlicht in:	arXiv.org 2021-01
Hauptverfasser:	Hayashi, Kao, Obuchi, Tomoyuki, Kabashima, Yoshiyuki
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Computer Science - Learning Cost function Greedy algorithms Monte Carlo simulation Physics - Disordered Systems and Neural Networks Reconstruction Regression analysis Search algorithms Statistics - Machine Learning
Online-Zugang:	Volltext
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