L1 norm based multiplication-free cosine similarity measures for big data analysis
Author
Akbaş, Cem Emre
Bozkurt, Alican
Arslan, Musa Tunç
Aslanoğlu, Hüseyin
Çetin, A. Enis
Date
2014-11Source Title
International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014
Publisher
IEEE
Pages
[1] - [5]
Language
English
Type
Conference PaperItem Usage Stats
162
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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 dataCosine 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/11693/28674Published Version (Please cite this version)
http://dx.doi.org/10.1109/IWCIM.2014.7008798Collections
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