Independent component analysis is intended to recover the unknown components as independent as possible from their linear mixtures. This technique has been widely used in many fields, such as data analysis, signal processing, and machine learning. In this paper, we present a novel boosting-based algorithm for independent component analysis. Our algorithm fills the gap in the nonparametric independent component analysis by introducing boosting to maximum likelihood estimation. A variety of experiments validate its performance compared with many of the presently known algorithms.

翻译：独立部件分析旨在尽可能从线性混合物中取回不为人知的部件,这种技术在数据分析、信号处理和机器学习等许多领域被广泛使用。在本文中,我们为独立部件分析提出了一个新的基于推进的算法。我们的算法通过引入最大可能性的估算来填补非对称独立部件分析的空白。各种实验都验证其与许多目前已知的算法相比的性能。