Boosting Independent Component Analysis
This work addresses a common challenge in data analysis and signal processing, but appears incremental as it builds on existing nonparametric methods.
The paper tackles the problem of recovering independent components from linear mixtures without prior knowledge by proposing a boosting-based algorithm that combines likelihood maximization and fixed-point methods, and validates its performance through experiments.
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.