Novel fast random search clustering approach for mixing matrix identification in MIMO linear blind inverse problems with sparse inputs

Luengo García, David and Monzón García, Sandra and Artés Rodríguez, Antonio (2012). Novel fast random search clustering approach for mixing matrix identification in MIMO linear blind inverse problems with sparse inputs. "Neurocomputing", v. 87 (n. null); pp. 62-78. ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2012.02.007.

Description

Title: Novel fast random search clustering approach for mixing matrix identification in MIMO linear blind inverse problems with sparse inputs
Author/s:
  • Luengo García, David
  • Monzón García, Sandra
  • Artés Rodríguez, Antonio
Item Type: Article
Título de Revista/Publicación: Neurocomputing
Date: June 2012
ISSN: 0925-2312
Volume: 87
Subjects:
Freetext Keywords: Linear blind inverse problems; Sparse signals; Line orientation clustering; MIMO systems; Neyman–Pearson hypothesis test
Faculty: E.U.I.T. Telecomunicación (UPM)
Department: Ingeniería de Circuitos y Sistemas [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In this paper we propose a novel fast random search clustering (RSC) algorithm for mixing matrix identification in multiple input multiple output (MIMO) linear blind inverse problems with sparse inputs. The proposed approach is based on the clustering of the observations around the directions given by the columns of the mixing matrix that occurs typically for sparse inputs. Exploiting this fact, the RSC algorithm proceeds by parameterizing the mixing matrix using hyperspherical coordinates, randomly selecting candidate basis vectors (i.e. clustering directions) from the observations, and accepting or rejecting them according to a binary hypothesis test based on the Neyman–Pearson criterion. The RSC algorithm is not tailored to any specific distribution for the sources, can deal with an arbitrary number of inputs and outputs (thus solving the difficult under-determined problem), and is applicable to both instantaneous and convolutive mixtures. Extensive simulations for synthetic and real data with different number of inputs and outputs, data size, sparsity factors of the inputs and signal to noise ratios confirm the good performance of the proposed approach under moderate/high signal to noise ratios. RESUMEN. Método de separación ciega de fuentes para señales dispersas basado en la identificación de la matriz de mezcla mediante técnicas de "clustering" aleatorio.

More information

Item ID: 22660
DC Identifier: http://oa.upm.es/22660/
OAI Identifier: oai:oa.upm.es:22660
DOI: 10.1016/j.neucom.2012.02.007
Official URL: http://www.sciencedirect.com/science/article/pii/S0925231212000744
Deposited by: Memoria Investigacion
Deposited on: 18 Mar 2014 16:59
Last Modified: 21 Apr 2016 17:39
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