Ultralow‐Dimensionality Reduction for Identifying Critical Transitions by Spatial‐Temporal PCA

Ultralow‐Dimensionality Reduction for Identifying Critical Transitions by Spatial‐Temporal PCA

Abstract Discovering dominant patterns and exploring dynamic behaviors especially critical state transitions and tipping points in high‐dimensional time‐series data are challenging tasks in study of real‐world complex systems, which demand interpretable data representations to facilitate comprehensi...

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Bibliographic Details
Main Authors: Pei Chen, Yaofang Suo, Kazuyuki Aihara, Ye Li, Dan Wu, Rui Liu, Luonan Chen
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:Advanced Science
Subjects:
critical state transition
interpretable data representation
spatial‐temporal PCA
ultralow‐dimensionality reduction
Online Access:https://doi.org/10.1002/advs.202408173
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https://doi.org/10.1002/advs.202408173

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