PCA of probability measures: Sparse and Dense sampling regimes

A common approach to perform PCA on probability measures is to embed them into a Hilbert space where standard functional PCA techniques apply.

While convergence rates for estimating the embedding of a single measure from $m$ samples are well understood, the literature has not addressed the setting involving multiple measures.

In this paper, we study PCA in a double asymptotic regime where $n$ probability measures are observed, each through $m$ samples.

We derive convergence rates of the form $n^{-1/2} + m^{-\alpha}$ for the empirical covariance operator and the PCA excess risk, where $\alpha>0$ depends on the chosen embedding.

This characterizes the relationship between the number $n$ of measures and the number $m$ of samples per measure, revealing a sparse (small $m$) to dense (large $m$) transition in the convergence behavior.

Moreover, we prove that the dense-regime rate is minimax optimal for the empirical covariance error.

Our numerical experiments validate these theoretical rates and demonstrate that appropriate subsampling preserves PCA accuracy while reducing computational cost.