Dimensionality reduction for dimension-specific search
http://data.open.ac.uk/oro/11971
is a Article
, Academic article
Outgoing links | |
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| Property | Object |
| Creator | |
| Dataset | Open Research Online |
| At | 30th Annual International ACM SIGIR conference on Research and Development in information retrieval |
| Date | 2007 |
| Is part of | repository |
| Status | Peer reviewed |
| URI |
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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. |
| Authors | authors |
| Type | |
| Label |
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| Same as | 1277741.1277940 |
| Title | Dimensionality reduction for dimension-specific search |