Robust Parallel Fast-ICA Algorithms Using Batch and Adaptive MMSE Estimators

dc.creatorMessina, Francisco
dc.creatorCernuschi-Frías, Bruno
dc.date2012-08
dc.date2012
dc.date2021-09-01T14:25:09Z
dc.date.accessioned2023-07-15T03:02:51Z
dc.date.available2023-07-15T03:02:51Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/123933
dc.identifierhttps://41jaiio.sadio.org.ar/sites/default/files/21_AST_2012.pdf
dc.identifierissn:1850-2806
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7464243
dc.descriptionAll the algorithms for ICA require high-order statistics to estimate the independent components. This is because second-order information is insufficient to assess that two random variables are independent of each other. It is known that the robustness of the high-order sample estimators is poor, meaning that a few outliers can change dramatically its value. In this paper, we generalize the alternative robust statistics for moments and cumulants introduced by Welling presenting the MMSE-robust moments. Then we present a batch and adaptive versions of an algorithm for estimating the parameters that define the estimator. Finally, we modify two FastICA algorithms of ICA based on kurtosis and negentropy to apply the MMSE robust estimators and show some experiments with supergaussian sources to demonstrate the improvement.
dc.descriptionSociedad Argentina de Informática e Investigación Operativa
dc.formatapplication/pdf
dc.format240-251
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.subjectCiencias Informáticas
dc.subjectICA
dc.subjectAlgorithm
dc.subjectBatch and Adaptive MMSE Estimators
dc.titleRobust Parallel Fast-ICA Algorithms Using Batch and Adaptive MMSE Estimators
dc.typeObjeto de conferencia
dc.typeObjeto de conferencia


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