Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unlabelled Sensing: A Sparse Bayesian Learning Approach   We address the recovery of sparse vectors in an overcomplete, linear and
noisy multiple measurement framework, where the measurement matrix is known
upto a permutation of its rows. We derive sparse Bayesian learning (SBL) based
updates for joint recovery of the unknown sparse vector and the sensing order,
represented using a permutation matrix. We model the sparse matrix using
multiple uncorrelated and correlated vectors, and in particular, we use the
first order AR model for the correlated sparse vectors. We propose the
Permutation-MSBL and a Kalman filtering based Permutation-KSBL algorithm for
low-complex joint recovery of the uncorrelated and correlated sparse vectors,
jointly with the permutation matrix. The novelty of this work emerges in
providing a simple update step for the permutation matrix using the
rearrangement inequality. We demonstrate the mean square error and the
permutation recovery performance of the proposed algorithms using Monte Carlo
simulations.

22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph