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

@article{Boukouvalas2018IndependentCA,
  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)},
  year={2018},
  pages={403-407}
}
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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References

SHOWING 1-10 OF 14 REFERENCES
Development of ICA and IVA Algorithms with Application to Medical Image Analysis
TLDR
This work introduces a flexible ICA algorithm that uses an effective PDF estimator to accurately capture the underlying statistical properties of the data and discusses several techniques to accurately estimate the parameters of the multivariate generalized Gaussian distribution, and how to integrate them into the IVA model. Expand
Fast and robust fixed-point algorithms for independent component analysis
  • A. Hyvärinen
  • Mathematics, Computer Science
  • IEEE Trans. Neural Networks
  • 1999
TLDR
Using maximum entropy approximations of differential entropy, a family of new contrast (objective) functions for ICA enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. Expand
Independent Component Analysis by Entropy Bound Minimization
  • Xi-Lin Li, T. Adalı
  • Mathematics, Computer Science
  • IEEE Transactions on Signal Processing
  • 2010
A novel (differential) entropy estimator is introduced where the maximum entropy bound is used to approximate the entropy given the observations, and is computed using a numerical procedure thusExpand
Diversity in Independent Component and Vector Analyses: Identifiability, algorithms, and applications in medical imaging
TLDR
This overview article presents ICA, and then its generalization to multiple data sets, IVA, both using mutual information rate, and presents conditions for the identifiability of the given linear mixing model and derive the performance bounds. Expand
Blind separation of piecewise stationary non-Gaussian sources
TLDR
A novel ICA algorithm called Block EFICA is proposed that is a further extension of the popular non-Gaussianity-based FastICA algorithm and of its recently optimized variant called EFICA that is based on this generalized model of signals. Expand
Handbook of Blind Source Separation: Independent Component Analysis and Applications
TLDR
This handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Expand
ICA Using Spacings Estimates of Entropy
TLDR
A new algorithm for the independent components analysis (ICA) problem based on an efficient entropy estimator that is simple, computationally efficient, intuitively appealing, and outperforms other well known algorithms. Expand
Density estimation by entropy maximization with kernels
TLDR
The maximum entropy principle is used to achieve this goal and present a density estimator that is based on two types of approximation, where Gaussian kernels are used as local measuring functions and parameters are estimated by expectation maximization and a new probability difference measure. Expand
Blind separation and blind deconvolution: an information-theoretic approach
  • A. J. Bell, T. Sejnowski
  • Mathematics, Computer Science
  • 1995 International Conference on Acoustics, Speech, and Signal Processing
  • 1995
TLDR
A new algorithm is derived and with it it perform nearly perfect separation of up to 10 digitally mixed human speakers, better performance than any previous algorithms for blind separation. Expand
Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics
TLDR
A face-image, pair-matching approach primarily developed and tested on the “Labeled Faces in the Wild” benchmark that reflects the challenges of face recognition from unconstrained images, and describes a number of novel, effective similarity measures. Expand
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