L1 norm based multiplication-free cosine similarity measures for big data analysis
Computational Intelligence for Multimedia Understanding(2014)
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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