ICA Fixed-Point Algorithm (FastICA)

Application Context: This algorithm is derived from fixed-point recursive methodology and is applicable to any data type. Its development enables ICA analysis of high-dimensional data. Also known as the Fixed-Point algorithm, it was proposed by Hyvärinen et al. from University of Helsinki. FastICA employs batch processing where substantial sample data participates in each iteration, making it a rapid optimization iterative algorithm. While distinct from conventional neural networks, it can still be categorized as a neural network algorithm from distributed parallel processing perspective. FastICA exists in multiple forms including fourth-order cumulant-based, maximum likelihood-based, and maximum negentropy-based implementations.