Huang, Zi; Hengtao, Shen; Zhou, Xiaofang; Song, Dawei and Rüger, Stefan
Dimensionality reduction for dimension-specific search.
In: 30th Annual International ACM SIGIR conference on Research and Development in information retrieval, 23-27 July 2007, Amsterdam, The Netherlands, p. 849.
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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.
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