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Enhanced trajectory data visualization: a dynamic time warping integrated t-SNE approach with real-data applications
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Publication Year
2024-01-01
Journal
Communications in Statistics: Simulation and Computation
Publisher
Taylor and Francis Ltd.
Citation
Communications in Statistics: Simulation and Computation
Keyword
Dynamic time warpingt-Distributed stochastic neighborhood embeddingTrajectory dataVisualization
Mesh Keyword
Curved structureData applicationDistance measureDynamic time warpingEmbeddingsNeighbourhoodRobust distanceStochasticsT-distributed stochastic neighborhood embeddingTrajectory data
All Science Classification Codes (ASJC)
Statistics and ProbabilityModeling and Simulation
Abstract
In this study, we focus on visualizing trajectory data, a type of data based on a series of temporal observations. We present an adapted version of t-distributed stochastic neighborhood embedding (t-SNE) tailored for trajectory data. This method is designed to preserve the inherent curved structure of trajectory data by incorporating a robust distance measure. Furthermore, it demonstrates the ability to maintain the data structure even in the presence of missing values at different time points. The performance of the proposed method is rigorously evaluated through a simulation study and demonstrated its effectiveness in visualizing two types of trajectory data, Gait data and NBA data.
ISSN
1532-4141
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/34629
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85210528418&origin=inward
DOI
https://doi.org/10.1080/03610918.2024.2430732
Journal URL
http://www.tandf.co.uk/journals/titles/03610918.asp
Type
Article
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Ahn, Soohyun Image
Ahn, Soohyun안수현
Department of Mathematics
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