A roughness model for functional data reveals a phase transition beyond which FPCA loses all information about the underlying variation.
Signal Processing , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Augmenting multimodal pediatric sleep embeddings with PHATE trajectories, persistent homology, movement descriptors, and EHR improves AUPRC and calibration for predicting desaturation, EEG arousal, hypopnea, and apnea.
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Does PCA Work for Rough Functional Data?
A roughness model for functional data reveals a phase transition beyond which FPCA loses all information about the underlying variation.
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Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings
Augmenting multimodal pediatric sleep embeddings with PHATE trajectories, persistent homology, movement descriptors, and EHR improves AUPRC and calibration for predicting desaturation, EEG arousal, hypopnea, and apnea.