Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings
Qijia Jiang
2022Year
3Citations
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext e1300a52-0da5-41b3-9b24-4a534654c859Builds on1
Related papers
- Tensor Concentration Inequalities: A Geometric ApproachAfonso S. Bandeira, Sivakanth Gopi, Haotian Jiang, Kevin Lucca et al.STOC 2025 · 1 citation
- Sharp Recovery Thresholds of Tensor PCA Spectral AlgorithmsMichael Feldman, David L. DonohoNeurIPS 2023 · 1 citation
- Matrix Chaos Inequalities and Chaos of Combinatorial TypeAfonso S. Bandeira, Kevin Lucca, Petar Nizic-Nikolac, Ramon van HandelSTOC 2025 · 4 citations
- Beyond the Signs: Nonparametric Tensor Completion via Sign SeriesChanwoo Lee, Miaoyan WangNeurIPS 2021 · 6 citations
- Nonparametric Decomposition of Sparse TensorsConor Tillinghast, Shandian ZheICML 2021 · 10 citations
