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Dimensionality reduction for dimension-specific search.

Huang, Zi; Shen, Hengtao; Zhou, Xiaofang; Song, Dawei; Rüger, Stefan

Authors

Zi Huang

Hengtao Shen

Xiaofang Zhou

Dawei Song

Stefan Rüger



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.

Presentation Conference Type Poster
Start Date Jul 23, 2007
Publisher Association for Computing Machinery
Pages 849-850
Institution Citation HUANG, Z., SHEN, H., ZHOU, X., SONG, D. and RUGER, S. 2007. Dimensionality reduction for dimension-specific search. In Proceedings of the 30th Annual international Association of Computing Machinery Special Interest Group on Information Retrieval (ACM SIGIR) conference on research and development in information retrieval (SIGIR'07), 23-27 July 2007, Amsterdam, Netherlands. New York: ACM [online], pages 849-850. Available from: https://doi.org/10.1145/1277741.1277940
DOI https://doi.org/10.1145/1277741.1277940
Keywords Algorithms; Retrieval models; Scientific databases

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