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

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      Author
      Akbaş, Cem Emre
      Bozkurt, Alican
      Arslan, Musa Tunç
      Aslanoğlu, Hüseyin
      Çetin, A. Enis
      Date
      2014-11
      Source Title
      International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014
      Publisher
      IEEE
      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.
      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
      Permalink
      http://hdl.handle.net/11693/28674
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/IWCIM.2014.7008798
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      • Department of Electrical and Electronics Engineering 3601
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