2022
NIPS
NeurIPS 2022
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.
🌉
Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
🧭
Keyword Pioneer
— random tensor embedding
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy