A self-attention imputation model combined with a convolutional autoencoder refinement fills missing segments in smooth pursuit eye movements more accurately than PCHIP, SSA, and KNN, especially for long gaps.
A survey on missing data in machine learning
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Imputation of Missing Data in Smooth Pursuit Eye Movements Using a Self-Attention-based Deep Learning Approach
A self-attention imputation model combined with a convolutional autoencoder refinement fills missing segments in smooth pursuit eye movements more accurately than PCHIP, SSA, and KNN, especially for long gaps.