SAX2FACE: Estimating Facial Poses with Peano-Hilbert Curves and SAX Symbolic Time Series

Mekami, Hayet; Benabderrahmane, Sidahmed

doi:10.1016/j.procs.2017.05.335

Cited by 3 publications

(2 citation statements)

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“…In the proposed algorithm, a genetic algorithm is employed to determine the nearly optimal configuration of breakpoints that provides the optimal fitness during the SAX process. Additional variations of SAX are described in [ 37 , 38 , 39 , 40 , 41 ].…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, SAX has a major limitation in which it only represents the mean value of each segment in the time-series. Thus, SAX representation is prone to missing some important information in the time-series [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Especially, in classification, one of the main research topics in time-series analysis, retention of meaningful information is critical because the classification performance would be significantly affected if the symbols between different classes are ambiguously discriminated.…”

Section: Introductionmentioning

confidence: 99%

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Distance- and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification

Yang

Kang

2022

Sensors

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Time-series representation is the most important task in time-series analysis. One of the most widely employed time-series representation method is symbolic aggregate approximation (SAX), which converts the results from piecewise aggregate approximation to a symbol sequence. SAX is a simple and effective method; however, it only focuses on the mean value of each segment in the time-series. Here, we propose a novel time-series representation method—distance- and momentum-based symbolic aggregate approximation (DM-SAX)—that can secure time-series distributions by calculating the perpendicular distance from the time-axis to each data point and consider the time-series trend by adding a momentum factor reflecting the direction of previous data points. Experimental results for 29 highly imbalanced classification problems on the UCR datasets revealed that DM-SAX affords the optimal area under the curve (AUC) among competing time-series representation methods (SAX, extreme-SAX, overlap-SAX, and distance-based SAX). We statistically verified that performance improvements resulted in significant differences in the rankings. In addition, DM-SAX yielded the optimal AUC for real-world wire cutting and crimping process dataset. Meaningful data points such as outliers could be identified in a time-series outlier detection framework via the proposed method.

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Section: Related Workmentioning

confidence: 99%