Structure sensitive complexity for symbol-free sequences

The study proposes our extended method to assess structure complexity for symbol-free sequences, such as literal texts, DNA sequences, rhythm, and musical input. This method is based on L-system and topological entropy for context-free grammar. Inputs are represented as binary trees. Different input...

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Bibliographic Details
Published in:Applied informatics 2015-07, Vol.2 (1), p.1-17, Article 6
Main Authors: Liou, Cheng-Yuan, Simak, Aleksandr A, Liou, Jiun-Wei
Format: Article
Language:English
Subjects:
Artificial Intelligence
Bioinformatics
Complexity
Computer Applications
Computer Imaging
Computer Science
Deoxyribonucleic acid
DNA
Gene sequencing
Health Informatics
Health Sciences
Medicine
Music
Pattern Recognition and Graphics
Rhythm
Statistics for Life Sciences
Texts
Vision
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Summary:The study proposes our extended method to assess structure complexity for symbol-free sequences, such as literal texts, DNA sequences, rhythm, and musical input. This method is based on L-system and topological entropy for context-free grammar. Inputs are represented as binary trees. Different input features are represented separately within tree structure and actual node contents. Our method infers tree generating grammar and estimates its complexity. This study reviews our previous results on texts and DNA sequences and provides new information regarding them. Also, we show new results measuring complexity of Chinese classical texts and music samples with rhythm and melody components. Our method demonstrates enough sensitivity to extract quasi-regular structured fragments of Chinese texts and to detect irregular styled samples of music inputs. To our knowledge, there is no other method that can detect such quasi-regular patterns.
ISSN:2196-0089
2196-0089
DOI:10.1186/s40535-015-0011-9