Boosting Independent Component Analysis
Machine Learning
2022-07-13 v3 Machine Learning
Signal Processing
Abstract
Independent component analysis is intended to recover the mutually independent components from their linear mixtures. This technique has been widely used in many fields, such as data analysis, signal processing, and machine learning. To alleviate the dependency on prior knowledge concerning unknown sources, many nonparametric methods have been proposed. In this paper, we present a novel boosting-based algorithm for independent component analysis. Our algorithm consists of maximizing likelihood estimation via boosting and seeking unmixing matrix by the fixed-point method. A variety of experiments validate its performance compared with many of the presently known algorithms.
Cite
@article{arxiv.2112.06920,
title = {Boosting Independent Component Analysis},
author = {Yunpeng Li and ZhaoHui Ye},
journal= {arXiv preprint arXiv:2112.06920},
year = {2022}
}