Model-based clustering for multivariate functional data

By J. Jacques, Cristian Preda. Computational Statistics & Data Analysis, 2013

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Functional data clustering: a survey (2013, methodology): “Using a similar approximation of the notion of density for functional random variables, Bouveyron and Jacques (2011) and Jacques and Preda (2013a) assume a Gaussian distribution of the principal components and deﬁne probabilistic model-based clustering techniques by the mean of the following…”
Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains (2015, methodology): “Single observations xi of X can then be characterized by their score vectors (ρi,1, . . . , ρi,M ) with ρi,m = 〈〈xi, ψm〉〉 for further analysis, for example, for regression (Müller and Stadtmüller 2005) or clustering (Jacques and Preda 2014).”
Functional Data Analysis (2016, methodology): “Similarly, Jacques & Preda (2013, 2014) applied the idea of Gaussian mixture modeling to FPCA scores.”
Selected statistical methods of data analysis for multivariate functional data (2018, methodology): “Berrenderoa et al. (2011), Jacques and Preda (2014) and Panaretos et al. (2010) discussed principal component analysis for multivariate functional data (MFPCA).”