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Journal Articles IEEE Transactions on Information Theory Year : 2019

A Data-Dependent Weighted LASSO Under Poisson Noise


Sparse linear inverse problems appear in a variety of settings, but often the noise contaminating observations cannot accurately be described as bounded by or arising from a Gaussian distribution. Poisson observations in particular are a characteristic feature of several real-world applications. Previous work on sparse Poisson inverse problems encountered several limiting technical hurdles. This paper describes a novel alternative analysis approach for sparse Poisson inverse problems that 1) sidesteps the technical challenges present in previous work, 2) admits estimators that can readily be computed using off-the-shelf LASSO algorithms, and 3) hints at a general framework for broad classes of noise in sparse linear inverse problems. At the heart of this new approach lies a weighted LASSO estimator for which data-dependent weights are based on Poisson concentration inequalities. Unlike previous analyses of the weighted LASSO, the proposed analysis depends on conditions which can be checked or shown to hold in general settings with high probability. 2000 Math Subject Classification: 60E15, 62G05, 62G08, and 94A12.

Dates and versions

hal-02175582 , version 1 (05-07-2019)



Xin Jiang Hunt, Patricia Reynaud-Bouret, Vincent Rivoirard, Laure Sansonnet, Rebecca Willett. A Data-Dependent Weighted LASSO Under Poisson Noise. IEEE Transactions on Information Theory, 2019, 65 (3), pp.1589-1613. ⟨10.1109/TIT.2018.2869578⟩. ⟨hal-02175582⟩
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