Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Underdetermined Blind Identification for $k$-Sparse Component Analysis
  using RANSAC-based Orthogonal Subspace Search   Sparse component analysis is very popular in solving underdetermined blind
source separation (UBSS) problem. Here, we propose a new underdetermined blind
identification (UBI) approach for estimation of the mixing matrix in UBSS.
Previous approaches either rely on single dominant component or consider $k
\leq m-1$ active sources at each time instant, where $m$ is the number of
mixtures, but impose constraint on the level of noise replacing inactive
sources. Here, we propose an effective, computationally less complex, and more
robust to noise UBI approach to tackle such restrictions when $k = m-1$ based
on a two-step scenario: (1) estimating the orthogonal complement subspaces of
the overall space and (2) identifying the mixing vectors. For this purpose, an
integrated algorithm is presented to solve both steps based on Gram-Schmidt
process and random sample consensus method. Experimental results using
simulated data show more effectiveness of the proposed method compared with the
existing algorithms.

36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat