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