In this work, we introduce AMUSET-TICA, a CV-identification method that leverages leading time-lagged Independent Components (tICs) as input features for AMUSEt. We also show that the need for manual input feature selection can be eliminated by automatically generating elementary nonlinear basis functions of Gaussian type based on the statistical distributions of input features using a Gaussian Mixture Model. We also demonstrate that the performance of AMUSET-TICA can be improved by involving more Gaussian basis functions; thus we adopted a basis-stacking approach to increase the number of Gaussians for each tIC by applying all Gaussian basis functions obtained from the Gaussian Mixture Model to each tIC. These advancements allow AMUSET-TICA to efficiently identify CVs that accurately capture the slowest dynamical modes of biomolecules. We demonstrate AMUSET-TICA in three biomolecular systems: alanine dipeptide, NTL9, and the FIP35 WW domain. In each case, we show that the timescales corresponding to the CVs of AMUSET-TICA match well with the previously reported reference timescales obtained from well-validated Markov State Models (MSMs) containing a large number of states, and that AMUSET-TICA significantly outperforms both tICA and AMUSEt. For NTL9 and the FIP35 WW domain, we also show that the performance of AMUSET-TICA is comparable to deeplearning-based methods like VAMPnets. Additionally, we show that the CVs generated by AMUSET-TICA can be used to directly visualize the slowest dynamical processes in large molecular systems. Finally, we show that AMUSET-TICA is significantly more computationally efficient than VAMPnets in terms of CPU time.