Stabilizing the Recall in Similarity Search

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Type Article in Proceedings
Conference Fourth International Conference on Similarity Search and Applications, SISAP 2011
MU Faculty or unit

Faculty of Informatics

Field Informatics
Keywords locality-sensitive hashing; metric space; similarity search; recall; stability;
Description The recent techniques for approximate similarity search focus on optimizing answer precision/recall and they typically improve the average of these measures over a set of sample queries. However, according to our observation, the recall for particular indexes and queries can fluctuate considerably. In order to stabilize the recall, we propose a query-evaluation model that exploits several variants of the search index. This approach is applicable to a signicant subset of current approximate methods with a focus on techniques based purely on metric postulates. Applying this approach to the M-Index structure, we perform extensive measurements on large datasets and we show that this approach has a positive impact on the recall stability and it suppresses the most unsatisfactory cases. Further, the results indicate that the proposed approach can also increase the general average recall for given overall search costs.
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