Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization

  title={Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization},
  author={Zois Boukouvalas and Yuri Levin-Schwartz and Rami Mowakeaa and Gengshen Fu and T. Adalı},
  journal={2018 IEEE Statistical Signal Processing Workshop (SSP)},
Independent component analysis (ICA) is one of the most popular methods for blind source separation with a diverse set of applications, such as: biomedical signal processing, video and image analysis, and communications. The success of ICA is tied to proper characterization of the probability density function (PDF) of the latent sources; information that is generally unknown. In this work, we propose a new and efficient ICA algorithm based on entropy maximization with kernels, (ICA-EMK), which… Expand
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