Among the numerous techniques useful for the **data** representation or decomposition of time-evolving datasets, we may distinguish several seminal techniques like those based on variance's decomposition such as principal component analysis (PCA) [3] and its probabilistic formulation [4] or nonlinear extensions [5][6][7]; techniques using eigenfunctions of Markov matrices reflecting the (local) geometry and density of the **data** [8,9]; or more recent approaches exploiting the Koopman operator theory [10][11][12].