Huang, Zi; Hengtao, Shen; Zhou, Xiaofang; Song, Dawei and Rüger, Stefan
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|DOI (Digital Object Identifier) Link:||http://doi.org/10.1145/1277741.1277940|
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Dimensionality reduction plays an important role in efficient similarity search, which is often based on k-nearest neighbor (k-NN) queries over a high-dimensional feature space. In this paper, we introduce a novel type of k-NN query, namely conditional k-NN (ck-NN), which considers dimension-specific constraint in addition to the inter-point distances. However, existing dimensionality reduction methods are not applicable to this new type of queries. We propose a novel Mean-Std (standard deviation) guided Dimensionality Reduction (MSDR) to support a pruning based efficient ck-NN query processing strategy. Our preliminary experimental results on 3D protein structure data demonstrate that the MSDR method is promising.
|Item Type:||Conference Item|
|Academic Unit/Department:||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)
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|Date Deposited:||09 Oct 2008 12:58|
|Last Modified:||02 Aug 2016 16:31|
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