Paper No: 64
mixing matrix in Sparse Component Analysis based on converting
a multiple dominant to a single dominant problem
Author(s): Nima Noorshams, Babaie-Zadeh Massoud, Christian Jutten
We propose a new method for estimating the mixing matrix, A, in the linear model x(t)=A s(t), t=1,...,T$, for the problem of underdetermined Sparse Component Analysis (SCA). Contrary to most previous algorithms, there can be more than one dominant source at each instant (we call it a ``multiple dominant'' problem). The main idea is to convert the multiple dominant problem to a series of single dominant problems, which may be solved by well-known methods. Each of these single dominant problems results in the determination of some columns of A. This results in a huge decrease in computations, which lets us to solve higher dimension problems that were not possible before.