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|DOI (Digital Object Identifier) Link:||https://doi.org/10.1007/978-3-642-00887-0_60|
|Google Scholar:||Look up in Google Scholar|
Observing that current Global Similarity Measures (GSM) which average the effect of few significant differences on all dimensions may cause possible performance limitation, we propose the first Dimension-specific Similarity Measure (DSM) to take local dimension-specific constraints into consideration. The rationale for DSM is that significant differences on some individual dimensions may lead to different semantics. An efficient search algorithm is proposed to achieve fast Dimension-specific KNN (DKNN) retrieval. Experiment results show that our methods outperform traditional methods by large gaps.
|Item Type:||Conference Item|
|Copyright Holders:||2009 Springer-Verlag Berlin Heidelberg|
|Extra Information:||Published in Lecture Notes in Computer Science, 2009, Volume 5463/2009, 693-698|
|Academic Unit/School:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
|Depositing User:||Kay Dave|
|Date Deposited:||04 Jan 2011 12:44|
|Last Modified:||29 Nov 2016 16:00|
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