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Title: Dimensionality reduction for dimension-specific search.
Authors: Huang, Zi
Shen, Heng Tao
Zhou, Xiaofang
Song, Dawei
Ruger, Stefan
Keywords: Algorithms
Retrieval models
Scientific databases
Issue Date: 2007
Publisher: ACM
Citation: HUANG, Z., SHEN, H., ZHOU, X., SONG, D. and RUGER, S. 2007. Dimensionality reduction for dimension-specific search. In: C. CLARKE, N. FUHR, N. KANDO, W. KRAAIJ and A. DE VRIES, eds. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 23-27 July 2007. Amsterdam. pp. 849-850
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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