Publication 2008 Issue No. 9 - September Abstract - Effective Proximity Retrieval by Ordering Permutations
Effective Proximity Retrieval by Ordering Permutations
September 2008 (vol. 30 no. 9)
pp. 1-1
 ASCII Text x "Effective Proximity Retrieval by Ordering Permutations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 9, pp. 1-1, September, 2008.
 BibTex x @article{ 10.1109/TPAMI.2007.70815,author = {},title = {Effective Proximity Retrieval by Ordering Permutations},journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},volume = {30},number = {9},issn = {0162-8828},year = {2008},pages = {1-1},doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70815},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on Pattern Analysis and Machine IntelligenceTI - Effective Proximity Retrieval by Ordering PermutationsIS - 9SN - 0162-8828SP1EP1EPD - 1-1PY - 2008KW - Extraterrestrial measurementsKW - Pattern recognitionKW - DatabasesKW - Computer SocietyKW - Feature extractionKW - Information retrievalKW - Support vector machinesKW - Support vector machine classificationKW - Neural networksKW - SequencesKW - ImplementationKW - Data StructuresKW - Data Storage RepresentationsKW - Indexing methodsKW - Information Storage and RetrievalKW - Information Search and RetrievalVL - 30JA - IEEE Transactions on Pattern Analysis and Machine IntelligenceER -
We introduce a new probabilistic proximity search algorithm for range and A"-nearest neighbor (A"-NN) searching in both coordinate and metric spaces. Although there exist solutions for these problems, they boil down to a linear scan when the space is intrinsically high dimensional, as is the case in many pattern recognition tasks. This, for example, renders the A"-NN approach to classification rather slow in large databases. Our novel idea is to predict closeness between elements according to how they order their distances toward a distinguished set of anchor objects. Each element in the space sorts the anchor objects from closest to farthest to it and the similarity between orders turns out to be an excellent predictor of the closeness between the corresponding elements. We present extensive experiments comparing our method against state-of-the-art exact and approximate techniques, both in synthetic and real, metric and nonmetric databases, measuring both CPU time and distance computations. The experiments demonstrate that our technique almost always improves upon the performance of alternative techniques, in some cases by a wide margin.

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Index Terms:
Extraterrestrial measurements,Pattern recognition,Databases,Computer Society,Feature extraction,Information retrieval,Support vector machines,Support vector machine classification,Neural networks,Sequences,Implementation,Data Structures,Data Storage Representations,Indexing methods,Information Storage and Retrieval,Information Search and Retrieval
Citation:
"Effective Proximity Retrieval by Ordering Permutations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 9, pp. 1-1, Sept. 2008, doi:10.1109/TPAMI.2007.70815