[R] UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction • r/MachineLearning

Can I ask you a dumb question? I was thinking about dimensionality reduction the other day and an idea occurred to me: why not just use an autoencoder NN squeezing input data into d dimensions (d 2, 3, ...) and an appropriate loss function to mimic either PCA or t-SNE, or maybe even UMAP would work? This produces a scalable, incremental (approximate) algorithm that easily supports parallelisation. Besides being slower than a pure C/C implementation, do you see something wrong with it?