Variational Garrote for Sparse Inverse Problems
Sparse regularization plays a central role in solving inverse problems arising from incomplete or corrupted measurements. Different regularizers correspond to different prior assumptions about the structure of the unknown signal, and reconstruction performance depends on how well these priors match the intrinsic sparsity of the data. This work investigates the effect of sparsity priors in inverse problems by comparing conventional L1 regularization with the Variational Garrote (VG), a probabilistic method that approximates L0 sparsity through variational binary gating variables. A unified experimental framework is constructed across multiple reconstruction tasks including signal resamplin...
| Authors | Kanghun Lee, Hyungjoon Soh, Junghyo Jo |
|---|---|
| Primary category | stat.ML |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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