Dimensionality reduction for dimension-specific search
http://data.open.ac.uk/oro/11971
is a Article , Academic article

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Dataset Open Research Online
At 30th Annual International ACM SIGIR conference on Research and Development in information retrieval
Date 2007
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Status Peer reviewed
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  • http://data.open.ac.uk/oro/document/1780
  • http://data.open.ac.uk/oro/document/19874
  • http://data.open.ac.uk/oro/document/24955
  • http://data.open.ac.uk/oro/document/8719
Abstract 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.
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  • Huang, Zi; Hengtao, Shen; Zhou, Xiaofang; Song, Dawei and Rüger, Stefan (2007). Dimensionality reduction for dimension-specific search. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR 07 SIGIR 07, p. 849.
  • Huang, Zi; Hengtao, Shen; Zhou, Xiaofang; Song, Dawei and Rüger, Stefan (2007). Dimensionality reduction for dimension-specific search. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR 07 SIGIR 07, p. 849.
Same as 1277741.1277940
Title Dimensionality reduction for dimension-specific search