Tham khảo tài liệu 'adaptive filtering applications part 13', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 352 Adaptive Filtering Applications transformation between the data and the features to be determined. Central limit theorem guarantees that a linear combination of variables has a distribution that is closer to a Gaussian than that of any individual variable. Assuming that the features to be estimated are independent and non-Gaussian but possibly one of them the independent components can be determined by applying to the data the linear transformation that maps them into features with distribution which is as far as possible from Gaussian. Thus a measure of non-Gaussianity is used as an objective function to be maximized by a given numerical optimization technique with respect to possible linear transformations of the input data. Different methods have been developed considering different measures of Gaussianity. The most popular methods are based on measuring kurtosis negentropy or mutual information Hyvarinen 1999 Mesin et al. 2011 . Another interesting algorithm was proposed in Koller and Sahami 1996 . The mutual information of the features is minimized in line with IGA approach using a backward elimination procedure where at each state the feature which can be best approximated by the others is eliminated iteratively see Pasero Mesin 2010 for an air pollution application of this method . Thus in this case the mutual information of the input data is explored but there is no transformation of them as done instead by IGA . A further method based on mutual information is that of looking for the optimal input set for modelling a certain system selecting the variables providing maximal information on the output. Thus in this case the information that the input data have on the output is explored and features are again selected without being transformed or linearly combined. However selecting the input variables in term of their mutual information with the output raises a major redundancy issue. To overcome this problem an algorithm was developed in Sharma 2000 to .
