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Subspace Embeddings Under Nonlinear Transformations

Abstract

We consider low-distortion embeddings for subspaces under \emph{entrywise nonlinear transformations}. In particular we seek embeddings that preserve the norm of all vectors in a space S={y:y=f(x) for xZ}S = \{y: y = f(x)\text{ for }x \in Z\}, where ZZ is a kk-dimensional subspace of Rn\mathbb{R}^n and f(x)f(x) is a nonlinear activation function applied entrywise to xx. When ff is the identity, and so SS is just a kk-dimensional subspace, it is known that, with high probability, a random embedding into O(k/ϵ2)O(k/\epsilon^2) dimensions preserves the norm of all ySy \in S up to (1±ϵ)(1\pm \epsilon) relative error. Such embeddings are known as \emph{subspace embeddings}, and have found widespread use in compressed sensing and approximation algorithms. We give the first low-distortion embeddings for a wide class of nonlinear functions ff. In particular, we give additive ϵ\epsilon error embeddings into O(klog(n/ϵ)ϵ2)O(\frac{k\log (n/\epsilon)}{\epsilon^2}) dimensions for a class of nonlinearities that includes the popular Sigmoid SoftPlus, and Gaussian functions. We strengthen this result to give relative error embeddings under some further restrictions, which are satisfied e.g., by the Tanh, SoftSign, Exponential Linear Unit, and many other `soft' step functions and rectifying units. Understanding embeddings for subspaces under nonlinear transformations is a key step towards extending random sketching and compressing sensing techniques for linear problems to nonlinear ones. We discuss example applications of our results to improved bounds for compressed sensing via generative neural networks.

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