NavigatingtheEffectofParametrization forDimensionalityReduction

Parametric dimensionality reduction methods have gained prominence for their ability togeneralize tounseen datasets, anadvantage that traditional approaches typically lack. Despite their growing popularity, there remains a prevalent misconception among practitioners about the equivalence in performance between parametric and non-parametric methods. Here, we showthat these methods are not equivalent - parametric methods retain global structure but lose significant localdetails.