Independent Component Analysis based on multiple data-weighting
Machine Learning
2019-06-04 v1 Machine Learning
Abstract
Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. In this paper we present Multiple-weighted Independent Component Analysis (MWeICA) algorithm, a new ICA method which is based on approximate diagonalization of weighted covariance matrices. Our idea is based on theoretical result, which says that linear independence of weighted data (for gaussian weights) guarantees independence. Experiments show that MWeICA achieves better results to most state-of-the-art ICA methods, with similar computational time.
Keywords
Cite
@article{arxiv.1906.00028,
title = {Independent Component Analysis based on multiple data-weighting},
author = {Andrzej Bedychaj and Przemysław Spurek and Łukasz Struskim and Jacek Tabor},
journal= {arXiv preprint arXiv:1906.00028},
year = {2019}
}