Nonlinear dimensionality reduction on ECG signals enables unsupervised personalized arrhythmia detection with high accuracy on 2D embeddings using standard algorithms on the MIT-BIH database.
Parametric UMAP embeddings for representation and semi-supervised learning,
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Supervised UMAP works well for classification but shows clear limitations in incorporating response information for regression tasks.
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Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias
Nonlinear dimensionality reduction on ECG signals enables unsupervised personalized arrhythmia detection with high accuracy on 2D embeddings using standard algorithms on the MIT-BIH database.
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A Comparative Study of UMAP and Other Dimensionality Reduction Methods
Supervised UMAP works well for classification but shows clear limitations in incorporating response information for regression tasks.