Linear Dimension Reduction Approximately Preserving Level-Sets of the 1-Norm

dc.contributor.advisor

Mukherjee, Sayan

dc.contributor.author

Casey, Michael P.

dc.date.accessioned

2019-06-07T19:49:46Z

dc.date.available

2019-06-07T19:49:46Z

dc.date.issued

2019

dc.department

Mathematics

dc.description.abstract

We choose a family of matrices F : \R^D \to \R^k and a metric \rho on \R^k such that with high

probability, \rho(F (x), F (y)) is a strictly concave increasing function of ||x − y||_1 > 8 \epsilon^2

for x, y \in \R^D , up to a multiplicative error of 1 ±\epsilon. In particular, if X is a set of N

points in \R^D , the target dimension k may be chosen as C ln^2 (N^{c+2})/(\epsilon^2(1 −\epsilon )^2), with

C a constant and \epsilon > N^{−c} , to ensure all pairs of points of X of distance at least 8\epsilon^2

are treated this way, with failure probability at most N^{-c} for c > 1. In some cases,

distances smaller than 8\epsilon^2 can also be addressed. For distances larger than \sqrt{1 +\epsilon} ,

the target dimension can be reduced to C ln(N^{c+2})/(\epsilon^2(1 −\epsilon )^2).

dc.identifier.uri

https://hdl.handle.net/10161/18836

dc.subject

Mathematics

dc.title

Linear Dimension Reduction Approximately Preserving Level-Sets of the 1-Norm

dc.type

Dissertation

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