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scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder with differentiable edge sampling. [PDF]

Biol Direct
Wang S, Liu Y, Zhang H, Liu Z.
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Quantile Symbolic Aggregate approXimation: A guaranteed equiprobable SAX

Anais do XXXVIII Simpósio Brasileiro de Banco de Dados (SBBD 2023), 2023
Time series are broadly present in science and industry. In specific scenarios, it is useful to classify series in order to gain knowledge regarding a specific range of values. In such cases, we often use symbolic representation, as it can reduce the data dimensionality creating representative symbols, making the data discrete and allowing specialized ...
Eduardo Silveira, Joaquim Assunção, Leonardo Emmendorfer   +2 more
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Human motion retrieval with symbolic aggregate approXimation

2012 24th Chinese Control and Decision Conference (CCDC), 2012
Motion capture data exhibits its complexity both spatially and temporally, which makes it a hard work to measure the similarities between human motions. We propose a motion data indexing and retrieval method based on self-organizing map and symbolic aggregate approximation.
null Qinkun Xiao, null Yichuang Luo, null Song Gao   +2 more
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Distribution-Wise Symbolic Aggregate ApproXimation (dwSAX)

2020
The Symbolic Aggregate approXimation algorithm (SAX) is one of the most popular symbolic mapping techniques for time series. It is extensively utilized in sequence classification, pattern mining, anomaly detection and many other data mining tasks. SAX as a powerful symbolic mapping technique is widely used due to its data adaptability.
Matej Kloska, Viera Rozinajova
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Multichannel Symbolic Aggregate Approximation Intelligent Icons: Application for Activity Recognition

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, Petros Karvelis, George Georgoulas, Chrysostomos Stylios   +3 more
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Trend-based symbolic aggregate approximation for time series representation

2018 Chinese Control And Decision Conference (CCDC), 2018
Due 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), 2017
Clustering 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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Rolling element bearings diagnostics using the Symbolic Aggregate approXimation

Mechanical Systems and Signal Processing, 2015
Abstract Rolling element bearings are a very critical component in various engineering assets. Therefore it is of paramount importance the detection of possible faults, especially at an early stage, that may lead to unexpected interruptions of the production or worse, to severe accidents. This research work introduces a novel, in the field of bearing
George Georgoulas, Petros Karvelis, Theodoros Loutas, Chrysostomos D. Stylios   +3 more
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Parallel symbolic aggregate approximation and its application in intelligent fault diagnosis

Journal of Intelligent & Fuzzy Systems, 2023
Fault 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, Chen, Yesheng, Liu, Shulin, Shen, Jiayi, Miao, Zhonghua   +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), 2020
Symbolic 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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