L1 norm based multiplication-free cosine similarity measures for big data analysis
International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014
1 - 5
Item Usage Stats
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.
Zitouni, Hilal; Sevil, Sare; Özkan, Derya; Duygulu, Pınar (IEEE, 2008-12)We propose a method to improve the results of image search engines on the Internet to satisfy users who desire to see relevant images in the first few pages. The method re-ranks the results of text based systems by ...
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 ...
Kesimal, Hayriye Sıla (Bilkent University, 2020-08)Self-similar sets are one class of fractals that are invariant under geometric similarities. In this thesis, we study on self-similar sets. We give the deﬁnition of a self-similar set K and present the proof the existence ...