Isomap is a nonlinear dimensionality reduction method. Laplacian Eigenmaps uses spectral techniques to perform dimensionality reduction. Such techniques can be applied to other nonlinear dimensionality reduction algorithms as well. However, conventional stochastic ADMM methods suffer from curse of dimensionality. Geometric anomalities in high dimension lead to the well-known curse of dimensionality. Quality of word embedding increases with higher dimensionality. The use of latent variables can serve to reduce the dimensionality of data. It is a form of non-linear dimensionality reduction. In this case, the optimization problem is said to have a low intrinsic dimensionality. Note that the dimensions of the pressure changes with dimensionality. A's strong/weak concept by attributing vectors of dimensionality to each function. The dimensionality of "F" is countably infinite. Regular polytopes are classified primarily according to their dimensionality. The equations have a high dimensionality (6D). The slow convergence here can be seen as a consequence of the curse of dimensionality.