A filtration-based shared structure learning framework organizes multiple functional predictors in a hierarchical forest to identify multiscale shared and predictor-specific components, improving prediction and revealing joint coordination patterns in applications like aging kinematics.
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Filtration-Based Learning of Multiscale Shared Structures for Multiple Functional Predictors
A filtration-based shared structure learning framework organizes multiple functional predictors in a hierarchical forest to identify multiscale shared and predictor-specific components, improving prediction and revealing joint coordination patterns in applications like aging kinematics.