The dimensionality of the tensor product 𝝍 is .1 + 𝑁 5 (1)/ × . ). Here, 𝑁 5 (𝑖) is the number of the nonlinear basis functions used for 𝑥 . . The dimensionality of 𝝍 grows exponentially with the number of input features 𝑁 / : 𝑁 6 ≥ 2 , % (the lower bound, 2 , % , corresponds to the situation where a single Gaussian function is chosen for each input feature, i.e., 1 + 𝑁 5 = 2). Consequently, it becomes impractical to store 𝝍 in computer memory for a large 𝑁 / . To address this issue, it was recognized in creation of AMUSEt 55 that the resulting .𝑁 6 × 𝑁 -/ -dimensional array of all basis-function evaluations at all MD frames possesses an exact representation in tensor-train format, which requires 𝑁 -⋅ ∑ .1 + 𝑁 5 (𝑖)/ , % .0! parameters. In addition, a two-step approach was proposed to first compress the tensor-train representation further, and then extract an orthogonal set of collective variables 𝝃, ranked by the timescales of their corresponding dynamical modes. This two-step method itself is called AMUSEt.