L1 norm based multiplication-free cosine similarity measures for big data analysis

Computational Intelligence for Multimedia Understanding(2014)

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摘要
The cosine similarity measure is widely used in big data analysis to compare vectors. In this article a new set of vector similarity measures are proposed. New vector similarity measures are based on a multiplication-free operator which requires only additions and sign operations. A vector `product' using the multiplication-free operator is also defined. The new vector product induces the ℓ1-norm. As a result, new cosine measure-like similarity measures are normalized by the ℓ1-norms of the vectors. They can be computed using the MapReduce framework. Simulation examples are presented.
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关键词
big data,vectors,l1 norm based multiplication-free cosine similarity measure,mapreduce framework,big data analysis,cosine measure-like similarity measure,multiplication-free operator,vector product,vector similarity measure,mapreduce,cosine similarity,accuracy,mathematical model
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