CoCo-Fused-Lasso for error-in-variables regression and application of denoising on tomography images

Thomas M. Menino Convention & Exhibition Center 

Sparse learning is a central topic in modern statistics, yet many widely used methods rely on assumptions that ignore measurement error and structural dependence in high-dimensional signals. Motivated by these limitations, we propose the CoCo–Fused Lasso, a structured sparse learning method that combines the convex correction framework of CoCoLasso with the fused Lasso penalty. Building on the work of Datta and Zou, the proposed approach addresses error-in-variables while simultaneously enforcing sparsity and piecewise-constant structure in the estimated parameters. This extension broadens the scope of sparse regression methods to settings where both measurement error and structured signals are present. The proposed estimator is formulated through a convex optimization problem and is computationally tractable. Simulation studies demonstrate improved estimation behavior relative to existing sparse estimators when structured signals are present. An application to denoising tomographic images of gold nanoparticles illustrates the practical benefits of the proposed method.

Sparse learning

CoCoLasso

Fused Lasso

Structured sparsity

Convex regularization

High-dimensional regression 

Section on Statistics in Imaging