  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.
