Can advanced algorithms improve biomedical data analysis? This article explores the use of high-order measures of independence within the framework of Independent Component Analysis (ICA). It primarily focuses on a set of Jacobi algorithms and discusses them for optimization. It also considers high-order measures of independence for the independent component analysis problem. Several implementations of high-order measures of independence within the framework of Independent Component Analysis (ICA) are discussed. The algorithmic point of view and also a set of biomedical data are used to compare the proposed approaches with gradient-based techniques. This comparison, conducted with biomedical data, sheds light on the strengths and weaknesses of these techniques. This research offers practical insights for researchers and practitioners in fields like neuroscience, signal processing, and machine learning. The class of Jacobi algorithms are helpful in certain problem sets. By enhancing the tools available for ICA, this work could ultimately lead to improved understanding of complex datasets across various scientific disciplines.
As a publication dedicated to computational neuroscience, Neural Computation is an ideal platform for this article. The paper focuses on a advanced class of Jacobi algorithms for optimization, making it of strong interest to neuroscientists and computer scientists working with ICA. This is relevant to neurosciences and computer science.