Efficient Estimation of Unique Components in Independent Component Analysis by Matrix Representation
This work addresses a bottleneck in ICA for signal processing and feature extraction applications, but it is incremental as it builds on prior methods for uniqueness estimation.
The paper tackles the problem of non-uniqueness in Independent Component Analysis (ICA) solutions by accelerating the estimation of unique components through matrix representation to reduce redundant calculations, achieving efficiency gains verified on artificial datasets and EEG data.
Independent component analysis (ICA) is a widely used method in various applications of signal processing and feature extraction. It extends principal component analysis (PCA) and can extract important and complicated components with small variances. One of the major problems of ICA is that the uniqueness of the solution is not guaranteed, unlike PCA. That is because there are many local optima in optimizing the objective function of ICA. It has been shown previously that the unique global optimum of ICA can be estimated from many random initializations by handcrafted thread computation. In this paper, the unique estimation of ICA is highly accelerated by reformulating the algorithm in matrix representation and reducing redundant calculations. Experimental results on artificial datasets and EEG data verified the efficiency of the proposed method.