Rank-Based Similarity Search Reducing the Dimensional Dependence - 2015


This paper introduces a data structure for k-NN search, the Rank Cowl Tree (RCT), whose pruning tests rely solely on the comparison of similarity values; different properties of the underlying space, like the triangle inequality, aren't utilized. Objects are selected in line with their ranks with respect to the query object, allowing abundant tighter management on the overall execution prices. A formal theoretical analysis shows that with very high probability, the RCT returns a correct question result in time that depends terribly competitively on a live of the intrinsic dimensionality of the data set. The experimental results for the RCT show that non-metric pruning methods for similarity search will be sensible even when the representational dimension of the information is very high. They conjointly show that the RCT is capable of meeting or exceeding the amount of performance of state-of-the-art strategies that make use of metric pruning or other choice tests involving numerical constraints on distance values.

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