Non-Negative Orthogonal Greedy Algorithms

Abstract : Orthogonal greedy algorithms are popular sparse signal reconstruction algorithms. Their principle is to select atoms one by one. A series of unconstrained least-squares subproblems of gradually increasing size is solved to compute the approximation coefficients, which is efficiently performed using a fast recursive update scheme. When dealing with non-negative sparse signal reconstruction, a series of non-negative least-squares subproblems have to be solved. Fast implementation becomes tricky since each subproblem does not have a closed-form solution anymore. Recently, non-negative extensions of the classical orthogonal matching pursuit and orthogonal least squares algorithms were proposed, using slow (i.e., non-recursive) or recursive but inexact implementations. In this paper, we revisit these algorithms in a unified way. We define a class of non-negative orthogonal algorithms and exhibit their structural properties. We propose a fast and exact implementation based on the active-set resolution of non-negative least-squares and exploiting warm start initializations. The algorithms are assessed in terms of accuracy and computational complexity for a sparse spike deconvolution problem. We also present an application to near-infrared spectra decomposition.
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Submitted on : Tuesday, February 26, 2019 - 1:44:34 PM
Last modification on : Monday, May 6, 2019 - 4:24:19 PM
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  • HAL Id : hal-02049424, version 1


Thi Thanh Nguyen, Jérôme Idier, Charles Soussen, El-Hadi Djermoune. Non-Negative Orthogonal Greedy Algorithms. 2019. ⟨hal-02049424⟩



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