On the Parzen Kernel-Based Probability Density Function Learning Procedures Over Time-Varying Streaming Data With Applications to Pattern Classification

Piotr Duda

doi:10.1109/TCYB.2018.2877611

2020-04-01

PRE

DOI：10.1109/TCYB.2018.2877611

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摘要

En 中文

In this paper, we propose a recursive variant of the Parzen kernel density estimator (KDE) to track changes of dynamic density over data streams in a nonstationary environment. In stationary environments, well-established traditional KDE techniques have nice asymptotic properties. Their existing extensions to deal with stream data are mostly based on various heuristic concepts (losing convergence properties). In this paper, we study recursive KDEs, called recursive concept drift tracking KDEs, and prove their weak (in probability) and strong (with probability one) convergence, resulting in perfect tracking properties as the sample size approaches infinity. In three theorems and subsequent examples, we show how to choose the bandwidth and learning rate of a recursive KDE in order to ensure weak and strong convergence. The simulation results illustrate the effectiveness of our algorithm both for density estimation and classification over time-varying stream data.

Keyword:

Kernel

Convergence

Neural networks

Bandwidth

Probability density function

Estimation

Probabilistic logic

Concept drift

convergence in probability and with probability one

online density estimation

pattern classification in nonstationary environments

time-varying environment

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期刊

IEEE Transactions on Cybernetics

IF:

10.5

论文数:

1.1W

被引数:

5.0W

机构

University of Social Sciences

学者数:

论文数: 114

被引数: 1

technical university czestochowa

学者数:

1.3K

论文数: 1.5K

被引数: 1

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