2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06) (2006)

Berkeley, California

Oct. 21, 2006 to Oct. 24, 2006

ISSN: 0272-5428

ISBN: 0-7695-2720-5

pp: 153-164

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FOCS.2006.79

David Arthur , Stanford University, USA

Sergei Vassilvitskii , Stanford University, USA

ABSTRACT

We show a worst-case lower bound and a smoothed upper bound on the number of iterations performed by the iterative closest point (ICP) algorithm. First proposed by Besl and McKay, the algorithm is widely used in computational geometry where it is known for its simplicity and its observed speed. The theoretical study of ICP was initiated by Ezra, Sharir and Efrat, who bounded its worst-case running time between Omega(n log n) and O(n

^{2}d)^{d}. We substantially tighten this gap by improving the lower bound to Omega(n/d)^{d+1 }. To help reconcile this bound with the algorithm's observed speed, we also show the smoothed complexity of ICP is polynomial, independent of the dimensionality of the data. Using similar methods, we improve the best known smoothed upper bound for the popular k-means method to n^{O(k)}once again independent of the dimensionINDEX TERMS

computational complexity,

CITATION

D. Arthur and S. Vassilvitskii, "Worst-case and Smoothed Analysis of the ICP Algorithm, with an Application to the k-means Method,"

*2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06)(FOCS)*, Berkeley, California, 2007, pp. 153-164.

doi:10.1109/FOCS.2006.79

CITATIONS