Research map / Learning theory and algorithms / Matrix and tensor methods
A convex program for bilinear inversion of sparse vectors
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ℓ1-BranchHull in the natural parameter space with known sign information | Sparse bilinear inversion with Gaussian dictionaries · Noiseless synthetic recovery | exact recovery | Helped |
| Add slack variables with an ℓ1 penalty | Robust ℓ1-BranchHull · Bilinear inversion with dense noise and arbitrary outliers | noise and outlier tolerance | Helped |
| Use total variation for the target signal instead of an ℓ1 coefficient penalty | TV ℓ1-BranchHull · Real distorted images | piecewise-constant signal recovery | Helped |
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