Efficient Estimation of Unique Components in Independent Component Analysis by Matrix Representation

TL;DR

This paper introduces a matrix-based approach to efficiently estimate unique components in ICA, significantly reducing computation time while maintaining accuracy, as validated on artificial and EEG datasets.

Contribution

The paper presents a novel matrix reformulation of ICA estimation that accelerates the process and ensures the uniqueness of the solution.

Findings

Significant reduction in computation time.

Successful application to EEG data.

Maintains accuracy of component estimation.

Abstract

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.