The Johnson-Lindenstrauss lemma states that for any n points in
Euclidean space and error parameter 0
All known proofs of the lemma construct a distribution over linear
mappings so that a random such mapping suffices with high
probability. In this talk, I will present various proofs of the JL
lemma satisfying, for example, (1) the support of the distribution
is small, so that a random embedding can be selected with few
random bits (e.g. O(log n loglog n) bits for constant eps, which is
suboptimal by a loglog n factor), and (2) every embedding matrix in
the support of the distribution is sparse (only O(eps*k) entries
per column are non-zero), to speed up computation. I will also
describe some open problems. This talk is based on joint works with
Daniel Kane (Harvard).