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Efficient data structures for density estimation for large high-dimensional data | IEEE Conference Publication | IEEE Xplore

Efficient data structures for density estimation for large high-dimensional data


Abstract:

Density estimation is a fundamental part of statistical analysis and data mining. In high-dimensional domains, parametric methods and the commonly used non-parametric met...Show More

Abstract:

Density estimation is a fundamental part of statistical analysis and data mining. In high-dimensional domains, parametric methods and the commonly used non-parametric methods like histograms or Kernel estimators fail to perform properly. In this paper, we present computationally efficient data structures for efficient implementation of the Bayesian sequential partitioning (BSP), as a framework for density estimation in high-dimensional domain. Simulation results are presented to analyze the performance for large high-dimensional datasets.
Date of Conference: 28-31 May 2017
Date Added to IEEE Xplore: 28 September 2017
ISBN Information:
Electronic ISSN: 2379-447X
Conference Location: Baltimore, MD, USA

References

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