DocumentCode
1056136
Title
Complexity analysis for partitioning nearest neighbor searching algorithms
Author
Zakarauskas, Pierre ; Ozard, John M.
Author_Institution
Dept. of Ophthalmology, British Columbia Univ., Vancouver, BC, Canada
Volume
18
Issue
6
fYear
1996
fDate
6/1/1996 12:00:00 AM
Firstpage
663
Lastpage
668
Abstract
Presents cost estimates for finding the k-nearest neighbors to a test pattern according to a Minkowski p-metric, as a function of the size of the buckets in partitioning searching algorithms. The asymptotic expected number of operations to find the nearest neighbor is presented as a function of the average number of patterns per bucket n and is shown to contain a global minimum
Keywords
computational complexity; pattern classification; search problems; Minkowski p-metric; complexity analysis; cost estimates; global minimum; nearest neighbor searching algorithms; partitioning; Algorithm design and analysis; Cost function; Distribution functions; Nearest neighbor searches; Neural networks; Partitioning algorithms; Pattern analysis; Pattern recognition; Search methods; Testing;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.506419
Filename
506419
Link To Document