Zohar Karnin, Yuval Rabani, Amir Shpilka

We construct a small set of explicit linear transformations mapping $R^n$ to $R^{O(\log n)}$, such that the $L_2$ norm of

any vector in $R^n$ is distorted by at most $1\pm o(1)$ in at

least a fraction of $1 - o(1)$ of the transformations in the set.

Albeit the tradeoff between ...
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Daniel Kane, Jelani Nelson

Recent work of [Dasgupta-Kumar-Sarl\'{o}s, STOC 2010] gave a sparse Johnson-Lindenstrauss transform and left as a main open question whether their construction could be efficiently derandomized. We answer their question affirmatively by giving an alternative proof of their result requiring only bounded independence hash functions. Furthermore, the sparsity bound obtained in ... more >>>