Nonlinear embeddings that are based on neighborhood graphs (such as t-SNE) are popular dimensionality reduction methods that can effectively visualize high-dimensional data. However, they are typically non-parametric and cannot be directly applied to out-of-sample points. This problem is typically addressed by learning a parametric mapping (e.g. neural nets) which projects inputs into low-dimensional manifold. Although a number of methods exist to train various mappings, only few of them conside…