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.
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/28674
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.
- Conference Paper 
Showing items related by title, author, creator and subject.
Şenel L.K.; Yücesoy V.; Koç A.; Çukur T. (Institute of Electrical and Electronics Engineers Inc., 2017)This paper studies cross-lingual semantic similarity (CLSS) between five European languages (i.e. English, French, German, Spanish and Italian) via unsupervised word embeddings from a cross-lingual lexicon. The vocabulary ...
Akbaş C.E.; Günay O.; Taşdemir K.; Çetin A.E. (Springer London, 2017)We propose a new family of vector similarity measures. Each measure is associated with a convex cost function. Given two vectors, we determine the surface normals of the convex function at the vectors. The angle between ...
Semantic similarity between Turkish and European languages using word embeddings [Türkçe ile Avrupa Dilleri Arasindaki Anlamsal Benzerliǧin Kelime Temsilleri ile Gösterimi] 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 ...