<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

A New Approach to Detection of Changes in Multidimensional Patterns

Department of Computer Science and Software Engineering

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"A New Approach to Detection of Changes in Multidimensional Patterns"}

Nowadays, unprecedented amounts of heterogeneous data collections are stored, processed and transmitted via the Internet. In data analysis one of the most...

A New Approach to Detection of Changes in Multidimensional Patterns

Publié en ligne: 20 mars 2020

Pages: 125 - 136

Reçu: 30 août 2019

Accepté: 25 févr. 2020

DOI: https://doi.org/10.2478/jaiscr-2020-0009

Mots clés
edge detection, regression, nonparametric estimation

© 2020 Tomasz Gałkowski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A New Approach to Detection of Changes in Multidimensional Patterns

Publié en ligne: 20 mars 2020

Pages: 125 - 136

Reçu: 30 août 2019

Accepté: 25 févr. 2020

DOI: https://doi.org/10.2478/jaiscr-2020-0009

Mots clésedge detection, regression, nonparametric estimation

© 2020 Tomasz Gałkowski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
edge detection, regression, nonparametric estimation