L1 norm based multiplication-free cosine similarity measures for big data analysis
2014 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014
Institute of Electrical and Electronics Engineers Inc.
Item Usage Stats
MetadataShow full item record
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.
Cosine similarity measures
Published Version (Please cite this version)http://dx.doi.org/10.1109/IWCIM.2014.7008798
Showing items related by title, author, creator and subject.
Aktürk, M. S. (Taylor & Francis, 1996)The existing studies in the literature usually ignore the within-cell layout problem while forming part families and manufacturing cells. A new approach is proposed to solve the part-family and machine-cell formation problem ...
Sjenel, L. K.; Yucesoy, V.; Koc, A.; Cukur, T. (Institute of Electrical and Electronics Engineers Inc., 2017)Representation of words coming from vocabulary of a language as real vectors in a high dimensional space is called as word embeddings. Word embeddings are proven to be successful in modelling semantic relations between ...
Saygin, Y.; Ulusoy, Ö. (IEEE, 2001)Fuzzy sets and fuzzy logic research aims to bridge the gap between the crisp world of math and the real world. Fuzzy set theory was applied to many different areas, from control to databases. Sometimes the number of events ...