Dimensionality reduction for dimension-specific search

Authors:
Zi Huang;Hengtao Shen;Xiaofang Zhou;Dawei Song;Stefan Rüger
Affiliations:
University of Queensland, Brisbane, Australia;University of Queensland, Brisbane, Australia;University of Queensland, Brisbane, Australia;The Open University, Milton Keynes, United Kingdom;The Open University, Milton Keynes, United Kingdom
Venue:
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2007

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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.