Sparse Independent Component Analysis
ICA has proven a useful tool for blind source separation and has been employed in many applications, however in certain scenarios, complete statistical independence can be too restrictive as an assumption. Additionally, important prior information about the data, such as sparsity, is usually available. Sparsity is a natural property of the data, a form of diversity, which, if incorporated into the ICA model, can relax the independence assumption, resulting in an improvement in the overall separation performance. The proposed algorithm, SparseICA by entropy bound minimization (SparseICA-EBM), inherits all the advantages of ICA-EBM, namely its flexibility, though with enhanced performance due to the exploitation of the sparsity of the underlying sources (when they are indeed sparse) and enables direct control over the degree to which independence and sparsity are emphasized.
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Sparse ICA by entropy bound minimization (SparseICA-EBM) (SparseICA_EBM) [1]
References:
[1] Z. Boukouvalas, Y. Levin-Schwartz, Vince D. Calhoun, and T. Adali, "Sparsity and Independence: Balancing of two Objectives in Optimization for Source Separation with Application to fMRI Analysis," Elsevier, Journal of the Franklin Institute (JFI), Engineering and Applied Mathematics, 2017.
[2] Z. Boukouvalas, Y. Levin-Schwartz, and T. Adali, "Enhancing ICA Performance By Exploiting Sparsity: Application to fMRI Analysis," In the proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, March 2017 pp 2532 - 2536.
