The concept of Privacy Safe Selection can be extended beyond functional data analysis to various other domains where model training involves choosing suitable hyperparameters or tuning settings without compromising data privacy.
For instance:
In non-parametric regression or classification problems: By employing PSS principles when selecting hyperparameters related to kernel functions or loss functions in learning algorithms, one can ensure that model training remains private without revealing sensitive information about individual datasets.
For kernel density estimation: Implementing PSS strategies when determining bandwidth matrices or kernel functions helps maintain differential privacy throughout density estimation processes.
By incorporating PSS methodologies into different machine learning tasks outside functional data analysis contexts, organizations and researchers can uphold stringent data protection standards while optimizing model performance effectively.