Online density estimation over high-dimensional stationary and non-stationary data streams
Document Type
Article
Publication Date
7-22-2019
Department
Department of Electrical and Computer Engineering
Abstract
Efficient density estimation over an open-ended stream of high-dimensional data is of primary importance to machine learning. In general, parametric methods for density estimation are not suitable for high dimensions, and the widely used non-parametric methods like kernel density estimation (KDE) method fail for high-dimensional datasets. In this paper we present a framework for density estimation over stationary and non-stationary high-dimensional data streams. It is based on a blockized implementation of the Bayesian sequential partitioning (BSP) algorithm. The proposed framework satisfies the general design criteria for systems with the mission of online machine learning and data mining over data streams.
Publication Title
Data & Knowledge Engineering
Recommended Citation
Majdara, A.,
&
Nooshabadi, S.
(2019).
Online density estimation over high-dimensional stationary and non-stationary data streams.
Data & Knowledge Engineering.
http://doi.org/10.1016/j.datak.2019.101718
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/574
Publisher's Statement
© 2019 Elsevier B.V. All rights reserved. Publisher’s version of record: https://doi.org/10.1016/j.datak.2019.101718