Online density estimation over high-dimensional data streams

Document Type

Conference Paper/Presentation

Publication Date



This paper presents an algorithm for density estimation over non-stationary high-dimensional data streams. It is based on a blockized implementation of the Bayesian sequential partitioning (BSP) algorithm. We discuss how to decide the optimum block size, based on features of the stream, the application requirements, and the available resources. Simulation results are presented to show the applicability of the proposed method to non-stationary streams. It is also shown that the proposed framework satisfies the general design criteria for systems with the mission of online machine learning and data mining over data streams.

Publisher's Statement

Publisher's version of record: https://dx.doi.org/10.1109/ISCAS.2019.8702795

Publication Title

2019 IEEE International Symposium on Circuits and Systems (ISCAS)