  In an inspiring paper Dubey and M\"uller (DM) extend PCA to the case that
observations are metric-valued functions. As an alternative, we develop a
kernel PCA approach, which we show is closely related to the DM approach. While
kernel principal components (kPCs) are simply defined, DM require added
complexity in the form of "object FPCs'' and "Fr\'echet scores".
