Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Blind Source Separation with Compressively Sensed Linear Mixtures   This work studies the problem of simultaneously separating and reconstructing
signals from compressively sensed linear mixtures. We assume that all source
signals share a common sparse representation basis. The approach combines
classical Compressive Sensing (CS) theory with a linear mixing model. It allows
the mixtures to be sampled independently of each other. If samples are acquired
in the time domain, this means that the sensors need not be synchronized. Since
Blind Source Separation (BSS) from a linear mixture is only possible up to
permutation and scaling, factoring out these ambiguities leads to a
minimization problem on the so-called oblique manifold. We develop a geometric
conjugate subgradient method that scales to large systems for solving the
problem. Numerical results demonstrate the promising performance of the
proposed algorithm compared to several state of the art methods.

23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th