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2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
In this work, we introduce the Multichannel Intelligent Icons, a novel method for producing and presenting essential patterns of multidimensional bio-signals. The proposed approach is an extension of Symbolic Aggregate Approximation (SAX) along with an innovative variation of Intelligent Icons.
Lamprini Pappa +3 more
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In this work, we introduce the Multichannel Intelligent Icons, a novel method for producing and presenting essential patterns of multidimensional bio-signals. The proposed approach is an extension of Symbolic Aggregate Approximation (SAX) along with an innovative variation of Intelligent Icons.
Lamprini Pappa +3 more
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Trend-based symbolic aggregate approximation for time series representation
2018 Chinese Control And Decision Conference (CCDC), 2018Due to high dimensionality and large volume of big time series data, the existing analysis technologies are poor for processing the raw data. The symbolic aggregate approximation (SAX) is one of the most powerful tools to deal with big time series data via reducing dimensionality.
Ke Zhang, Yuan Li, Yi Chai, Lei Huang
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Clustering of time series using hybrid symbolic aggregate approximation
2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017Clustering of time series is one of the best-known grand challenges in time series analysis because of its application potentialities and difficulty. It is like data clustering and the task of partitioning time series into several groups based on their similarities, such that time series in a cluster are similar and they are not similar to other ...
Keiichi Tamura, Takumi Ichimura
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Parallel symbolic aggregate approximation and its application in intelligent fault diagnosis
Journal of Intelligent & Fuzzy Systems, 2023Fault diagnosis is of great significance for industrial equipment maintenance, and feature extraction is a key step of the entire diagnosis scheme. The symbolic aggregate approximation (SAX) is a popular feature extraction approach with great potential recently.
Zhao, Dongfang +4 more
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Entropy-based Symbolic Aggregate Approximation Representation Method for Time Series
2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 2020Symbolic Aggregate approXimation (SAX) is one of the most common dimensionality reduction approaches for time-series and has been widely employed in lots of domains, including motif discovery, time-series classification, and fast shapelets discovery. However, SAX only considers the average value of the segment but ignores other essential features. As a
Haowen Zhang, Yabo Dong, Duanqing Xu
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Recognition of Signed Expressions Using Symbolic Aggregate Approximation
2014Complexity of sign language recognition system grows with growing word vocabulary. Therefore it is advisable to use units smaller than words. Such elements, called subunits, resemble phonemes in spoken language. They are concatenated to form word models.
Mariusz Oszust, Marian Wysocki
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A Musical Similarity Metric based on Symbolic Aggregate Approximation
2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2020We have continued our work in the field of AI-driven music synthesis and have improved upon our previous 3-layer gated recurrent unit neural network, with results confirming higher accuracy and much smaller validation loss. In order to achieve this, we have designed a recurrent neural architecture that is more suited to learning the musical style of J.
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SAX2SEX: Gender Classification on 3D Faces using Symbolic Aggregate ApproXimation
2019 6th International Conference on Image and Signal Processing and their Applications (ISPA), 2019Gender classification is a demographic attribute that found an increasing amount of applications particularly in human-computer interaction, security access control and biometrics. The purpose of this paper is to investigate the feasibility of using time series for gender classification.
Samia Bentaieb +2 more
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Genetic Algorithms-Based Symbolic Aggregate Approximation
2012Time series data appear in a broad variety of economic, medical, and scientific applications. Because of their high dimensionality, time series data are managed by using representation methods. Symbolic representation has attracted particular attention because of the possibility it offers to benefit from algorithms and techniques of other fields in ...
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Survey of Methods for Time Series Symbolic Aggregate Approximation
2019Time series analysis is widely used in the fields of finance, medical, and climate monitoring. However, the high dimension characteristic of time series brings a lot of inconvenience to its application. In order to solve the high dimensionality problem of time series, symbolic representation, a method of time series feature representation is proposed ...
Lin Wang +3 more
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