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

Discussion contribution "Functional models for time-varying random objects'' by Dubey and M\"uller (to appear in JRSS-B)