Sciweavers

Share
COMPGEOM
2004
ACM

Deterministic sampling and range counting in geometric data streams

9 years 5 months ago
Deterministic sampling and range counting in geometric data streams
We present memory-efficient deterministic algorithms for constructing -nets and -approximations of streams of geometric data. Unlike probabilistic approaches, these deterministic samples provide guaranteed bounds on their approximation factors. We show how our deterministic samples can be used to answer approximate online iceberg geometric queries on data streams. We use these techniques to approximate several robust statistics of geometric data streams, including Tukey depth, simplicial depth, regression depth, the Thiel-Sen estimator, and the least median of squares. Our algorithms use only a polylogarithmic amount of memory, provided the desired approximation factors are at least inversepolylogarithmic. We also include a lower bound for non-iceberg geometric queries.
Amitabha Bagchi, Amitabh Chaudhary, David Eppstein
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where COMPGEOM
Authors Amitabha Bagchi, Amitabh Chaudhary, David Eppstein, Michael T. Goodrich
Comments (0)
books