Summary: Independent Component Analysis (ICA) is an unsupervised technique for blind source separation, and the ICA algorithms using nongaussianity as the measure of mutual independence have been also used for projection pursuit or visualization of multivariate data for knowledge discovery in databases. However, in real applications, it is often the case that we fail to extract useful latent variables because they have no connection with predefined criterion variables. This paper proposes an enhanced technique of IGA, which extracts independent components closely related to some external criteria. Preprocessing is performed by using fuzzy regression-principal component analysis, which estimates latent variables that have high correlation with the external criteria considering local data structure.