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Related Experiment Video

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Big Data Blind Separation.

Mujahid N Syed1

  • 1Department of Systems Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.

Entropy (Basel, Switzerland)
|December 3, 2020
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Summary
This summary is machine-generated.

This study introduces a novel mathematical approach for non-negative data separation, enabling simultaneous validation of source assumptions and data separation. The method is efficient for big data analysis.

Keywords:
big datablind signal separationcorrentropy rankinglocally dominant sources

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Area of Science:

  • Data Analysis
  • Signal Processing

Background:

  • Non-negative data separation is crucial in data analysis.
  • Existing methods often lack validation for sparse locally dominant source assumptions.
  • Current techniques typically extract mixing matrix elements sequentially.

Purpose of the Study:

  • To develop a mathematical modeling-based approach for non-negative data separation.
  • To introduce a method that simultaneously validates the locally dominant source assumption and separates data.
  • To propose a correntropy-based measure for reducing model size in big data separation.

Main Methods:

  • Mathematical modeling for simultaneous validation and separation.
  • Correntropy-based measure for model size reduction.
  • Application to non-negative matrix factorization problems.

Main Results:

  • A novel approach for non-negative data separation is presented.
  • The method effectively validates the locally dominant source assumption.
  • The approach is suitable for big data separation and demonstrated through numerical experiments.

Conclusions:

  • The proposed mathematical modeling approach offers simultaneous validation and separation for non-negative data.
  • Correntropy-based measure enhances efficiency for big data applications.
  • This work provides a validated and efficient solution for a critical data analysis problem.