Literature DB >> 9950728

High-order contrasts for independent component analysis.

J F Cardoso1.   

Abstract

This article considers high-order measures of independence for the independent component analysis problem and discusses the class of Jacobi algorithms for their optimization. Several implementations are discussed. We compare the proposed approaches with gradient-based techniques from the algorithmic point of view and also on a set of biomedical data.

Mesh：

Year:  1999        PMID： 9950728     DOI： 10.1162/089976699300016863

Source DB:  PubMed          Journal:  Neural Comput        ISSN： 0899-7667            Impact factor:   2.026


  62 in total

1.  Effect of signal length on the performance of independent component analysis when extracting the lambda wave.

Authors:  L Vigon; R Saatchi; J E W Mayhew; N A Taroyan; J P Frisby
Journal:  Med Biol Eng Comput       Date:  2002-03       Impact factor: 2.602

2.  Detection of fast neuronal signals in the motor cortex from functional near infrared spectroscopy measurements using independent component analysis.

Authors:  G Morren; U Wolf; P Lemmerling; M Wolf; J H Choi; E Gratton; L De Lathauwer; S Van Huffel
Journal:  Med Biol Eng Comput       Date:  2004-01       Impact factor: 2.602

3.  Integrating independent component analysis with artificial neural network to analyze overlapping fluorescence spectra of organic pollutants.

Authors:  Ling Gao; Shouxin Ren
Journal:  J Fluoresc       Date:  2012-07-05       Impact factor: 2.217

4.  Adaptive spatial filtering of multichannel surface electromyogram signals.

Authors:  N Ostlund; J Yu; K Roeleveld; J S Karlsson
Journal:  Med Biol Eng Comput       Date:  2004-11       Impact factor: 2.602

5.  Extraction of gastric slow waves from electrogastrograms: combining independent component analysis and adaptive signal enhancement.

Authors:  H Liang
Journal:  Med Biol Eng Comput       Date:  2005-03       Impact factor: 2.602

6.  A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity.

Authors:  Srikantan S Nagarajan; Hagai T Attias; Kenneth E Hild; Kensuke Sekihara
Journal:  Neuroimage       Date:  2005-12-19       Impact factor: 6.556

7.  Reduction of noise from magnetoencephalography data.

Authors:  S Okawa; S Honda
Journal:  Med Biol Eng Comput       Date:  2005-09       Impact factor: 2.602

8.  Application of Independent Component Analysis Techniques in Speckle Noise Reduction of Retinal OCT Images.

Authors:  Ahmadreza Baghaie; Roshan M D'Souza; Zeyun Yu
Journal:  Optik (Stuttg)       Date:  2016-08       Impact factor: 2.443

9.  Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization.

Authors:  Aristeidis Sotiras; Susan M Resnick; Christos Davatzikos
Journal:  Neuroimage       Date:  2014-12-12       Impact factor: 6.556

10.  Independent Component Analysis Involving Autocorrelated Sources With an Application to Functional Magnetic Resonance Imaging.

Authors:  Seonjoo Lee; Haipeng Shen; Young Truong; Mechelle Lewis; Xuemei Huang
Journal:  J Am Stat Assoc       Date:  2012-01-24       Impact factor: 5.033
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