L1 norm based multiplication-free cosine similarity measures for big data analysis
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Date Issued
2014Journal Title
2014 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014
Publisher
Institute of Electrical and Electronics Engineers Inc.
Language
English
Type
Conference Paper
Please cite this item using this persistent URL
http://hdl.handle.net/11693/28674Abstract
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.
Published as
http://dx.doi.org/10.1109/IWCIM.2014.7008798Collections
- Conference Paper [2294]
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