Efficient blind source separation combining closed-form second-order ICA and nonclosed-form higher-order ICA

In this paper, first, we propose a computational-cost efficient blind source separation combining closed-form 2nd-order independent component analysis (ICA) and nonclosed-form higher-order ICA. The closed-form solution of the 2nd-order ICA has been recently presented by one of the authors. This finding motivates us to combine the closed-form 2nd-order ICA and higher-order ICA, where the preceding closed-form ICA produces a good initial value and the following higher-order ICA updates the separation filters from the advantageous status. Secondly, we utilize the proposed architecture to address an essential question that which type of statistics is more beneficial to ICA among non-stationarity and non-Gaussianity. This can be conducted owing to the attractive property that the closed-form ICA can provide a good estimate of the theoretical upper limitation of the separation performance among 2nd-order ICAs without suffering from poor-convergence problems. Experimental results reveal that the non-Gaussianity-based ICA can outperform the nonstationarity-based ICA.