All Dates/Times are Australian Eastern Standard Time (AEST)

# Technical Program

## Paper Detail

 Paper ID D4-S4-T3.1 Paper Title Multiple Support Recovery Using Very Few Measurements Per Sample Authors Lekshmi Ramesh, Chandra R. Murthy, Himanshu Tyagi, Indian Institute of Science, Bangalore, India Session D4-S4-T3: Sparse Recovery Chaired Session: Thursday, 15 July, 23:00 - 23:20 Engagement Session: Thursday, 15 July, 23:20 - 23:40 Abstract In the problem of multiple support recovery, we are given access to linear measurements of multiple sparse samples in $\mathbb{R}^{d}$. These samples can be partitioned into $\ell$ groups, with samples having the same support belonging to the same group. For a given budget of $m$ measurements per sample, the goal is to recover the $\ell$ underlying supports, in the absence of the knowledge of group labels. We study this problem with a focus on the \emph{measurement-constrained} regime where $m$ is smaller than the support size $k$ of each sample. We design a two-step procedure that estimates the union of the underlying supports first, and then uses a spectral algorithm to estimate the individual supports. Our proposed estimator can recover the supports with \$m