Diffusion maps is a nonlinear manifold (or latent variable) learning technique that can reveal the underlying nonlinear structures in high-dimensional data . The technique builds a diffusion, or a random walk, on the dataset by utilizing a connected graph between all points. Each connection is weighted by a similarity metric, W (x i , x j ), where similar points are connected by a large value (exactly 1 if x i = x j ), while the connection weight between dissimilar points approaches 0. Throughout this work, we have employed a Gaussian-like kernel defined as: