# Boosting Independent Component Analysis

arXiv: https://arxiv.org/abs/2112.06920 · HTML: https://mlsanity.com/paper/2112.06920/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 12 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Faster independent component analysis by preconditioning with Hessian approximations](https://mlsanity.com/paper/1706.08171/) (2017)
- [Overcomplete Independent Component Analysis via SDP](https://mlsanity.com/paper/1901.08334/) (2019)
- [Independent Component Analysis via Distance Covariance](https://mlsanity.com/paper/1306.4911/) (2013)
- [Boosting in Univariate Nonparametric Maximum Likelihood Estimation](https://mlsanity.com/paper/2101.08505/) (2021)
- [Second-order Approximation of Minimum Discrimination Information in Independent Component Analysis](https://mlsanity.com/paper/2111.15060/) (2021)

## Abstract (arXiv)

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.