Independent Component Analysis with Several Mixing Matrices
نویسنده
چکیده
A new algorithm is proposed for the variation of independent component analysis (ICA) in which there are several mixing matrices and, for each set of independent components, one of the matrices is randomly chosen to mix the components. The algorithm utilizes high-order moments and can obtain consistent estimators even if the true probability density function of independent components is not obtained. The effectiveness of our algorithm is verified by a numerical experiment. This method can be used to analyze a class of data generated overcompletely, and to classify data in an unsupervised manner.
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تاریخ انتشار 2001