# Improved Algorithms For Structured Sparse Recovery

## TL;DR

This paper introduces improved, efficient algorithms for structured sparse recovery in compressive sensing, specifically for tree sparsity and CEMD models, enhancing approximation accuracy and reducing computational complexity.

## Contribution

It presents the first linear-time approximation algorithms for head- and tail-approximations in structured sparsity models, solving open problems and improving recovery performance.

## Key findings

- Linear time $(1- ext{epsilon})$-approximation for tree sparsity head-approximation
- Linear time $(1+ ext{epsilon})$-approximation for tree sparsity tail-approximation
- First constant factor approximation for CEMD model head-approximation

## Abstract

It is known that certain structures of the signal in addition to the standard notion of sparsity (called structured sparsity) can improve the sample complexity in several compressive sensing applications. Recently, Hegde et al. proposed a framework, called approximation-tolerant model-based compressive sensing, for recovering signals with structured sparsity. Their framework requires two oracles, the head- and the tail-approximation projection oracles. The two oracles should return approximate solutions in the model which is closest to the query signal. In this paper, we consider two structured sparsity models and obtain improved projection algorithms. The first one is the tree sparsity model, which captures the support structure in the wavelet decomposition of piecewise-smooth signals. We propose a linear time $(1-\epsilon)$-approximation algorithm for head-approximation projection and a linear time $(1+\epsilon)$-approximation algorithm for tail-approximation projection. The best previous result is an $\tilde{O}(n\log n)$ time bicriterion approximation algorithm (meaning that their algorithm may return a solution of sparsity larger than $k$) by Hegde et al. Our result provides an affirmative answer to the open problem mentioned in the survey of Hegde and Indyk. As a corollary, we can recover a constant approximate $k$-sparse signal. The other is the Constrained Earth Mover Distance (CEMD) model, which is useful to model the situation where the positions of the nonzero coefficients of a signal do not change significantly as a function of spatial (or temporal) locations. We obtain the first single criterion constant factor approximation algorithm for the head-approximation projection. The previous best known algorithm is a bicriterion approximation. Using this result, we can get a faster constant approximation algorithm with fewer measurements for the recovery problem in CEMD model.