Orthogonal and non-orthogonal joint blind source separation in the least-squares sense

We present two new algorithms for the orthogonal and non-orthogonal joint blind source separation (JBSS), a flexible framework that extends the well-known blind source separation to the case when multiple datasets are decomposed simultaneously. The algorithms minimize the total sum of squares of the off-diagonal terms by means of very simple gradient ascent iterations.