Nonparametric Estimation of Continuously Parametrized Families of Probability Density Functions—Computational Aspects

Rafajłowicz, Wojciech

doi:10.3390/a13070164

Cited by 4 publications

(1 citation statement)

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“…Nonparametric approach to analysis and modelling of various systems one may found e.g. in [34,35,38,39,40]. Edge detection technique based on Parzen kernel estimate has also been described by Qiu in [32,33].…”

Section: A Brief Overview Of the Methodologies Used To Datementioning

confidence: 99%

A New Approach to Detection of Changes in Multidimensional Patterns

Gałkowski

Krzyżak

Filutowicz

2020

Journal of Artificial Intelligence and Soft Computing Research

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Nowadays, unprecedented amounts of heterogeneous data collections are stored, processed and transmitted via the Internet. In data analysis one of the most important problems is to verify whether data observed or/and collected in time are genuine and stationary, i.e. the information sources did not change their characteristics. There is a variety of data types: texts, images, audio or video files or streams, metadata descriptions, thereby ordinary numbers. All of them changes in many ways. If the change happens the next question is what is the essence of this change and when and where the change has occurred. The main focus of this paper is detection of change and classification of its type. Many algorithms have been proposed to detect abnormalities and deviations in the data. In this paper we propose a new approach for abrupt changes detection based on the Parzen kernel estimation of the partial derivatives of the multivariate regression functions in presence of probabilistic noise. The proposed change detection algorithm is applied to oneand two-dimensional patterns to detect the abrupt changes.

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