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

Date
2014-11
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Source Title
International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
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Pages
1 - 5
Language
English
Type
Conference Paper
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Abstract

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. © 2014 IEEE.

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Keywords
Big data, Cosine similarity, MapReduce, Multiplication-free operator, Artificial intelligence, Data handling, Information analysis, Vectors, Cosine similarity measures, Map-reduce, Mapreduce frameworks, Similarity measure, Simulation example, Vector similarity, Big data
Citation
Published Version (Please cite this version)