Implement algorithms for Blind Source Separation (BSS) and Independent Component Analysis (ICA) based on Approximate Joint Diagonalization (AJD) of matrices.
by Kevin for Shogun Machine Learning Toolbox
ICA/BSS can be done via the approximate joint diagonalization (AJD) of matrices. ADJ is the problem of finding a matrix, or set of basis vectors, that best diagonalizes a set of input matrices. It is an important tool playing a critical role in many applications including ICA and BSS. For machine learning in particular ICA can be used for pre-processing, automatic feature selection and dimensionality reduction for visualization. ADJ would be a valuable addition to the SHOGUN toolbox.