A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm
arXiv:1902.03736v1166 citations
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This work provides theoretical tools for analyzing high-dimensional data, but it is incremental as it generalizes existing results.
The authors tackled the problem of deriving concentration inequalities for random vectors with subGaussian norm, achieving results that are tight up to logarithmic factors.
In this note, we derive concentration inequalities for random vectors with subGaussian norm (a generalization of both subGaussian random vectors and norm bounded random vectors), which are tight up to logarithmic factors.