All these dimension reduction methods are extremely similar. The math essentially just preserves nearest neighbors, with a setting for how 'tight' you want the

All these dimension reduction methods are extremely similar. The math essentially just preserves nearest neighbors, with a setting for how 'tight' you want the clusters to be. Check out this image [1] and accompanying paper [2] for further reference [1] https://www.semanticscholar.org/paper/A-Unifying-Perspective-on-Neighbor-Embeddings-along-B%C3%B6hm-Berens/9411cea70d31834f6cb4d2e9bee205e02b56d938/figure/0 https://www.semanticscholar.org/paper/A-Unifying-Perspective... [2] https://arxiv.org/abs/2007.08902 https://arxiv.org/abs/2007.08902