In future studies, AMUSET-TICA can also serve as a CV-refinement method by utilizing CVs generated by techniques other than tICA as input features for further optimization. In addition, it would be interesting to systematically compare kernel-based and tensor-based approaches (e.g., AMUSET-TICA and AMUSEt) for identifying CVs. Kernel-based methods, such as kernel-tICA , adopt a different perspective by typically associating one basis function to each data point. This renders their scaling formally independent of the problem's dimensionality, but ultimately also requires the solution of large-scale linear algebra problems and careful tuning of hyperparameters.