1. | Luengo, David; Monzon, Sandra; Artés-Rodríguez, Antonio: Novel Fast Random Search Clustering Algorithm for Mixing Matrix Identification in MIMO Linear Blind Inverse Problems with Sparse Inputs. In: Neurocomputing, 87 , pp. 62–78, 2012. (Type: Journal Article | Abstract | Links | BibTeX) @article{Luengo2012b, title = {Novel Fast Random Search Clustering Algorithm for Mixing Matrix Identification in MIMO Linear Blind Inverse Problems with Sparse Inputs}, author = {David Luengo and Sandra Monzon and Antonio Artés-Rodríguez}, url = {http://www.tsc.uc3m.es/~antonio/papers/P43_2012_Novel Fast Random Search Clustering Algorithm for Mixing Matrix Identification in MIMO Linear Blind Inverse Problems with Sparse Inputs.pdf http://www.sciencedirect.com/science/article/pii/S0925231212000744}, year = {2012}, date = {2012-01-01}, journal = {Neurocomputing}, volume = {87}, pages = {62--78}, 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.}, keywords = {}, pubstate = {published}, tppubtype = {article} } 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. |

## 2012 |

## Journal Articles |

Luengo, David; Monzon, Sandra; Artés-Rodríguez, Antonio Novel Fast Random Search Clustering Algorithm for Mixing Matrix Identification in MIMO Linear Blind Inverse Problems with Sparse Inputs Journal Article Neurocomputing, 87 , pp. 62–78, 2012. Abstract | Links | BibTeX | Tags: Line orientation clustering, Linear blind inverse problems, MIMO systems, Neyman–Pearson hypothesis test, Sparse signals @article{Luengo2012b, title = {Novel Fast Random Search Clustering Algorithm for Mixing Matrix Identification in MIMO Linear Blind Inverse Problems with Sparse Inputs}, author = {David Luengo and Sandra Monzon and Antonio Artés-Rodríguez}, url = {http://www.tsc.uc3m.es/~antonio/papers/P43_2012_Novel Fast Random Search Clustering Algorithm for Mixing Matrix Identification in MIMO Linear Blind Inverse Problems with Sparse Inputs.pdf http://www.sciencedirect.com/science/article/pii/S0925231212000744}, year = {2012}, date = {2012-01-01}, journal = {Neurocomputing}, volume = {87}, pages = {62--78}, 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.}, keywords = {Line orientation clustering, Linear blind inverse problems, MIMO systems, Neyman–Pearson hypothesis test, Sparse signals}, pubstate = {published}, tppubtype = {article} } 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. |