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
Permalink (Please cite this version)http://hdl.handle.net/11693/28674
Showing items related by title, author, creator and subject.
Varol, E.; Can F.; Aykanat, C.; Kaya O. (2011)We study a generalized version of the near-duplicate detection problem which concerns whether a document is a subset of another document. In text-based applications, document containment can be observed in exact-duplicates, ...
Zitouni H.; Sevil, S.; Ozkan, D.; Duygulu P. (2008)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 ...
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 ...