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Date : 1998-10-31
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Independent Component Analysis Theory and Applications ~ Independent Component Analysis Theory and Applications is the first book to successfully address this fairly new and generally applicable method of blind source separation It is essential reading for researchers and practitioners with an interest in ICA
Independent Component Analysis Theory and Applications ~ Independent Component Analysis ICA is a signalprocessing method to extract independent sources given only observed data that are mixtures of the unknown sources Recently blind source separation by ICA has received considerable attention because of its potential signalprocessing applications such as speech enhancement systems telecommunications medical signalprocessing and several data mining issues
Independent Components Analysis Theory and Applications ~ Principal Components Analysis vs Independent Components Analysis ICA is often compared to Principal Components Analysis PCA The reason is that both methods aim to decompose a data matrix into two more informative matrices one characterizing the individuals rows and the other the variables columns by calculating linear combinations of the original variables
INDEPENDENT COMPONENTS ANALYSIS THEORY APPLICATIONS AND ~ Independent Components Analysis ICA is a blind source separation method that has been developed to extract the underlying source signals from a set of observed signals where they are mixed in unknown proportions
Independent Component Analysis and Its Applications ~ Independent Component Analysis ICA is a method to recover a version of the original sources by multiplying the data by a unmixing matrix u Wx where x is our observed signals a linear mixtures of sources x As While PCA simply decorrelates the outputs using an orthogonal matrix W ICA attempts to make the outputs
Applications of independent component analysis ~ Applications of independent component analysis 59 Figure 32 Prediction of realworld nancial data the upper gure represents the actual future outcome of one of the original mixtures and the lower one the forecast obtained using ICA prediction for an interval of 50 values
A review of independent component analysis application to ~ Independent component analysis ICA methods have received growing attention as effective datamining tools for microarray gene expression data As a technique of higherorder statistical analysis ICA is capable of extracting biologically relevant gene expression features from microarray data
Independent Component Analysis Algorithms and Applications ~ capture the essential structure of the data inmany applications including featureextraction and signal separation In this paper we present the basic theory and applications ofICAandourrecentworkonthesubject Keywords Independent component analysis projection pur suit blind signal separation source separation factor analysis representation
INDEPENDENT COMPONENT ANALYSIS Springer ~ Independent Component Analysis ICA is a signal processing method to extract independent sources given only observed data that are mixtures of the unknown sources Recently blind source separation by ICA has received attention because of its potential signal processing applications such as speech enhancement systems
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