An Analysis of Information Dynamic Behavior Using Autoregressive Models
Information Theory is a branch of mathematics, more specifically probability theory, that studies information quantification. Recently, several researches have been successful with the use of Information Theoretic Learning (ITL) as a new technique of unsupervised learning. In these works, informatio...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-11-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/19/11/612 |
| _version_ | 1798000055133667328 |
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| author | Amanda Oliveira Adrião D. Dória Neto Allan Martins |
| author_facet | Amanda Oliveira Adrião D. Dória Neto Allan Martins |
| author_sort | Amanda Oliveira |
| collection | DOAJ |
| description | Information Theory is a branch of mathematics, more specifically probability theory, that studies information quantification. Recently, several researches have been successful with the use of Information Theoretic Learning (ITL) as a new technique of unsupervised learning. In these works, information measures are used as criterion of optimality in learning. In this article, we will analyze a still unexplored aspect of these information measures, their dynamic behavior. Autoregressive models (linear and non-linear) will be used to represent the dynamics in information measures. As a source of dynamic information, videos with different characteristics like fading, monotonous sequences, etc., will be used. |
| first_indexed | 2024-04-11T11:14:10Z |
| format | Article |
| id | doaj.art-88754000b2494323b7071beb27b73d98 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T11:14:10Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-88754000b2494323b7071beb27b73d982022-12-22T04:27:19ZengMDPI AGEntropy1099-43002017-11-01191161210.3390/e19110612e19110612An Analysis of Information Dynamic Behavior Using Autoregressive ModelsAmanda Oliveira0Adrião D. Dória Neto1Allan Martins2Center of Exact and Natural Sciences, Federal Rural University of the Semi-Arid Region, Mossoro 59625-900, RN, BrazilDepartment of Automation and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, RN, BrazilDepartment of Electrical Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, RN, BrazilInformation Theory is a branch of mathematics, more specifically probability theory, that studies information quantification. Recently, several researches have been successful with the use of Information Theoretic Learning (ITL) as a new technique of unsupervised learning. In these works, information measures are used as criterion of optimality in learning. In this article, we will analyze a still unexplored aspect of these information measures, their dynamic behavior. Autoregressive models (linear and non-linear) will be used to represent the dynamics in information measures. As a source of dynamic information, videos with different characteristics like fading, monotonous sequences, etc., will be used.https://www.mdpi.com/1099-4300/19/11/612information theorydynamics processinformation potential |
| spellingShingle | Amanda Oliveira Adrião D. Dória Neto Allan Martins An Analysis of Information Dynamic Behavior Using Autoregressive Models Entropy information theory dynamics process information potential |
| title | An Analysis of Information Dynamic Behavior Using Autoregressive Models |
| title_full | An Analysis of Information Dynamic Behavior Using Autoregressive Models |
| title_fullStr | An Analysis of Information Dynamic Behavior Using Autoregressive Models |
| title_full_unstemmed | An Analysis of Information Dynamic Behavior Using Autoregressive Models |
| title_short | An Analysis of Information Dynamic Behavior Using Autoregressive Models |
| title_sort | analysis of information dynamic behavior using autoregressive models |
| topic | information theory dynamics process information potential |
| url | https://www.mdpi.com/1099-4300/19/11/612 |
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