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Dimensionality Reduction for Dimension-specific Search by Zi Huang; Hengtao Shen; Xiaofang Zhou; Dawei Song; Stefan Rüger is a scholarly article available to read on EtoBox.

What is Dimensionality Reduction for Dimension-specific Search about?

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.

Author

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

Publisher

ACM

Published

2007

Language

EN