Functional clustering by Bayesian wavelet methods

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Functional data clustering: a survey (2013, unknown): “Model on Type of model Reference FPCA scores Gaussian (parsimonious sub-models) [10] Gaussian [28, 29] Gaussian spherical (k-means) [14] basis expansion coefficients Gaussian (parsimonious) [30] Gaussian with regime changes [45] Bayesian [22, 25, 43]”
Functional clustering and identifying substructures of longitudinal data (2007, methodology): “These approaches may also take advantage of model-based techniques, as in the functional clustering (FC) models with special emphasis on sparsely sampled functional data (James and Sugar, 2003), the mixed effects modelling approach using B-splines (Luan and Li, 2003) and, very recently, the non-para”
Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing (2009, methodology): “Related models of this type have been employed previously for wavelet-based clustering [39].”
Hybrid Dirichlet mixture models for functional data (2009, background): “Bayesian hierarchical models and DP mixtures have been successfully exploited to represent the individual curves by means of an orthonormal basis expansion and to cluster the expansion coefficients (see Bigelow and Dunson (2009) and Ray and Mallick (2006) ).”