Literature DB >> 16121741

Sparse component analysis and blind source separation of underdetermined mixtures.

Pando Georgiev, Fabian Theis, Andrzej Cichocki.   

Abstract

In this letter, we solve the problem of identifying matrices S is an element of R(n x N) and A is an element of R(m x n) knowing only their multiplication X = AS, under some conditions, expressed either in terms of A and sparsity of S (identifiability conditions), or in terms of X (sparse component analysis (SCA) conditions). We present algorithms for such identification and illustrate them by examples.

Mesh:

Year:  2005        PMID: 16121741     DOI: 10.1109/TNN.2005.849840

Source DB:  PubMed          Journal:  IEEE Trans Neural Netw        ISSN: 1045-9227


  6 in total

1.  Motif-guided sparse decomposition of gene expression data for regulatory module identification.

Authors:  Ting Gong; Jianhua Xuan; Li Chen; Rebecca B Riggins; Huai Li; Eric P Hoffman; Robert Clarke; Yue Wang
Journal:  BMC Bioinformatics       Date:  2011-03-22       Impact factor: 3.169

2.  A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels.

Authors:  Ivica Kopriva; Marko Filipović
Journal:  BMC Bioinformatics       Date:  2011-12-30       Impact factor: 3.169

3.  Diagnosis of Compound Fault Using Sparsity Promoted-Based Sparse Component Analysis.

Authors:  Yansong Hao; Liuyang Song; Yanliang Ke; Huaqing Wang; Peng Chen
Journal:  Sensors (Basel)       Date:  2017-06-06       Impact factor: 3.576

4.  Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy.

Authors:  Yongzhen Du; Honggeng Yang; Xiaoyang Ma
Journal:  Entropy (Basel)       Date:  2020-01-03       Impact factor: 2.524

5.  Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial.

Authors:  Ariana Anderson; Mark S Cohen
Journal:  Front Hum Neurosci       Date:  2013-09-02       Impact factor: 3.169

6.  Underdetermined Blind Source Separation of Synchronous Orthogonal Frequency Hopping Signals Based on Single Source Points Detection.

Authors:  Chaozhu Zhang; Yu Wang; Fulong Jing
Journal:  Sensors (Basel)       Date:  2017-09-11       Impact factor: 3.576

  6 in total

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