Efficient data structures for density estimation for large high-dimensional data
Document Type
Conference Proceeding
Publication Date
9-25-2017
Abstract
© 2017 IEEE. 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.
Publication Title
Proceedings - IEEE International Symposium on Circuits and Systems
Recommended Citation
Majdara, A.,
&
Nooshabadi, S.
(2017).
Efficient data structures for density estimation for large high-dimensional data.
Proceedings - IEEE International Symposium on Circuits and Systems.
http://doi.org/10.1109/ISCAS.2017.8050592
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10673