NeurIPS 2022accept3 citations
Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings
Abstract
We give uniform concentration inequality for random tensors acting on rank-1 Kronecker structured signals, which parallels a Gordon-type inequality for this class of tensor structured data. Two variants of the random embedding are considered, where the embedding dimension depends on explicit quantities characterizing the complexity of the signal. As applications of the tools developed herein, we illustrate with examples from signal recovery and optimization.
Structured non-symmetric rank-1 tensorUniform deviation boundApplications of random tensor embeddings
BibTeX
@inproceedings{
jiang2022nearisometric,
title={Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings},
author={Qijia Jiang},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=cwWSpO6rl3Z}
}