Titlebook: Similarity Search and Applications; 7th International Co Agma Juci Machado Traina,Caetano Traina,Robson Leo Conference proceedings 2014 Spr

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Efficient Algorithms for Similarity Search in Axis-Aligned Subspaces search. Given a query object, the goal of subspace similarity search is to retrieve the most similar objects from the database, where the similarity distance is defined over an arbitrary subset of dimensions (or features) — that is, an arbitrary axis-aligned projective subspace. Though much effort

Osmosis

Partial Refinement for Similarity Search with Multiple Featuresse has one major drawback: when an object is refined, the partial distances to the query object are computed for .. This frequently leads to more distance computations being executed than necessary to exclude an object. To address this problem, we introduce ., a simple, yet efficient improvement of

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Some Theoretical and Experimental Observations on Permutation Spaces and Similarity Search data objects whose permutation representation is similar to the query one. Various permutation-based indexes have been recently proposed. They typically allow high efficiency with acceptable effectiveness. Moreover, various parameters can be set in order to find an optimal trade-off between quality

橡子

Metric Space Searching Based on Random Bisectors and Binary Fingerprintsos where the main memory available is small. The method was tested on synthetic and real-world metric spaces. Our results show that our scheme outperforms the standard permutant-based index in scenarios where memory is scarce.

多骨

Faster Proximity Searching with the Distal SATes instead of the proximal nodes proposed in the original paper. Our approach is parameter free and it was the most competitive in an extensive benchmarking, from two to forty times faster than the ., and faster than the List of Clusters (.) which is considered the state of the art for main memory,

羽饰

A Dynamic Pivoting Algorithm Based on Spatial Approximation Indexes, in particular, are efficient data structures, which have shown to be competitive in metric spaces of medium to high difficulty, or queries with low selectivity. . can be also made dynamic, and can use the available space to improve the query performance adding pivot information. In this paper we e

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Large-Scale Distributed Locality-Sensitive Hashing for General Metric Datad for just a handful of dissimilarities for which locality-sensitive families are available. In this work we propose Parallel Voronoi LSH, an approach that addresses those two limitations of LSH: it makes LSH efficient for distributed-memory architectures, and it works for very general dissimilariti