Connect the dots: How many random points can a regular curve pass through?

被引:18
作者
Arias-Castro, E [1 ]
Donoho, DL
Huo, XM
Tovey, CA
机构
[1] Stanford Univ, Dept Stat, Stanford, CA 94305 USA
[2] Georgia Inst Technol, Sch Ind & Syst Engn, Atlanta, GA 30332 USA
关键词
curve detection; filament detection; epsilon-entropy; configuration function; concentration of measure; longest increasing subsequence; traveling salesman problem; pattern recognition;
D O I
10.1239/aap/1127483737
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Given a class Gamma of curves in [0, 1](2), we ask: in a cloud of n uniform random points, how many points can lie on some curve gamma E I? Classes studied here include curves of length less than or equal to L, Lipschitz graphs, monotone graphs, twice-differentiable curves, and graphs of smooth functions with m-bounded derivatives. We find, for example, that there are twice-differentiable curves containing as many as O-P(n(1/3)) uniform random points, but not essentially more than this. More generally, we consider point clouds in higher-dimensional cubes [0, 1](d) and regular hypersurfaces of specified codimension, finding, for example, that twice-differentiable k-dimensional hypersurfaces in R-d may contain as many as O-P (n(k/(2d-k))) uniform random points. We also consider other notions of 'incidence', such as curves passing through given location/direction pairs, and find, for example, that twice-differentiable curves in R-2 may pass through at most O-P (n(1/4)) uniform random location/direction pairs. Idealized applications in image processing and perceptual psychophysics are described and several open mathematical questions are identified for the attention of the probability community.
引用
收藏
页码:571 / 603
页数:33
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