Independent Component Analysis by Entropy Maximization with Kernels
This addresses the issue of model mismatch in ICA for applications like biomedical signal processing, but it is incremental as it builds on existing entropy maximization and kernel methods.
The paper tackles the problem of poor source probability density function (PDF) estimation in independent component analysis (ICA), which leads to suboptimal performance, by proposing ICA-EMK, a new algorithm based on entropy maximization with kernels that dynamically estimates PDFs with reasonable complexity and enables parallel optimization. The result is demonstrated superior performance over competing ICA algorithms on simulated and real-world data, though no concrete numbers are provided.
Independent component analysis (ICA) is the most popular method for blind source separation (BSS) with a diverse set of applications, such as biomedical signal processing, video and image analysis, and communications. Maximum likelihood (ML), an optimal theoretical framework for ICA, requires knowledge of the true underlying probability density function (PDF) of the latent sources, which, in many applications, is unknown. ICA algorithms cast in the ML framework often deviate from its theoretical optimality properties due to poor estimation of the source PDF. Therefore, accurate estimation of source PDFs is critical in order to avoid model mismatch and poor ICA performance. In this paper, we propose a new and efficient ICA algorithm based on entropy maximization with kernels, (ICA-EMK), which uses both global and local measuring functions as constraints to dynamically estimate the PDF of the sources with reasonable complexity. In addition, the new algorithm performs optimization with respect to each of the cost function gradient directions separately, enabling parallel implementations on multi-core computers. We demonstrate the superior performance of ICA-EMK over competing ICA algorithms using simulated as well as real-world data.