Evaluating Model Misspecification in Independent Component Analysis.

ABSTRACT: Independent component analysis (ICA) is a popular blind source separation technique used in many scientific disciplines. Current ICA approaches have focused on developing efficient algorithms under specific ICA models, such as instantaneous or convolutive mixing conditions, intrinsically assuming temporal independence or autocorrelation of the sources. In practice, the true model is not known and different ICA algorithms can produce very different results. Although it is critical to choose an ICA model, there has not been enough research done on evaluating mixing models and assumptions, and how the associated algorithms may perform under different scenarios. In this paper, we investigate the performance of multiple ICA algorithms under various mixing conditions. We also propose a convoluti

SUBMITTER: Lee S 

PROVIDER: S-EPMC4309392 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature