Matrix deviation inequality for p-norm

Yuan Chung Sheu, Te Chun Wang

Research output: Contribution to journalArticlepeer-review


Motivated by the general matrix deviation inequality for i.i.d. ensemble Gaussian matrix [R. Vershynin, High-Dimensional Probability: An Introduction with Applications in Data Science, Cambridge Series in Statistical and Probabilistic Mathematics (Cambridge University Press, 2018), doi:10.1017/9781108231596 of Theorem 11.1.5], we show that this property holds for the p-norm with 1 ≤ p < ∞ and i.i.d. ensemble sub-Gaussian matrices, i.e. random matrices with i.i.d. mean-zero, unit variance, sub-Gaussian entries. As a consequence of our result, we establish the Johnson-Lindenstrauss lemma from 2n-space to pm-space for all i.i.d. ensemble sub-Gaussian matrices.

Original languageEnglish
Article number2350007
JournalRandom Matrices: Theory and Application
StateAccepted/In press - 2023


  • concentration inequality
  • Johnson-Lindenstrauss lemma
  • Matrix deviation inequality
  • sub-Gaussian matrix


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