A Hybridization of Simulated Annealing and Local PCA for Automatic Component Assignment Within ICA

Independent component analysis (ICA) as well as blind source separation (BSS) often faces the problem of assigning the independent or uncorrelated components estimated with ICA or BSS techniques to underlying source signals, artifacts or noise contributions. In this work an automatic assignment tool is presented which uses a priori knowledge about the form of some of the signals to be extracted. The algorithm is applied to the problem of removing water artifacts from 2D NOESY NMR spectra. The algorithm uses local PCA to approximate the water artifact and defines a suitable cost function which is optimized using simulated annealing. The blind source separation of the water artifact from the remaining protein spectrum is done with the recently developed algorithm dAMUSE.