L1 norm based multiplication-free cosine similarity measures for big data analysis
buir.contributor.author | Çetin, A. Enis | |
buir.contributor.orcid | Çetin, A. Enis|0000-0002-3449-1958 | |
dc.citation.epage | 5 | en_US |
dc.citation.spage | 1 | en_US |
dc.contributor.author | Akbaş, Cem Emre | en_US |
dc.contributor.author | Bozkurt, Alican | en_US |
dc.contributor.author | Arslan, Musa Tunç | en_US |
dc.contributor.author | Aslanoğlu, Hüseyin | en_US |
dc.contributor.author | Çetin, A. Enis | en_US |
dc.coverage.spatial | Paris, France | |
dc.date.accessioned | 2016-02-08T12:26:52Z | |
dc.date.available | 2016-02-08T12:26:52Z | |
dc.date.issued | 2014-11 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description | Date of Conference: 1-2 Nov. 2014 | |
dc.description | Conference name: International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014 | |
dc.description.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. | en_US |
dc.description.provenance | Made available in DSpace on 2016-02-08T12:26:52Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2014 | en |
dc.identifier.doi | 10.1109/IWCIM.2014.7008798 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/28674 | |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/IWCIM.2014.7008798 | en_US |
dc.source.title | International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014 | en_US |
dc.subject | Big data | en_US |
dc.subject | Cosine similarity | en_US |
dc.subject | MapReduce | en_US |
dc.subject | Multiplication-free operator | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Data handling | en_US |
dc.subject | Information analysis | en_US |
dc.subject | Vectors | en_US |
dc.subject | Cosine similarity measures | en_US |
dc.subject | Map-reduce | en_US |
dc.subject | Mapreduce frameworks | en_US |
dc.subject | Similarity measure | en_US |
dc.subject | Simulation example | en_US |
dc.subject | Vector similarity | en_US |
dc.subject | Big data | en_US |
dc.title | L1 norm based multiplication-free cosine similarity measures for big data analysis | en_US |
dc.type | Conference Paper | en_US |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- L1 norm based multiplication-free cosine similarity measures for big data analysis.pdf
- Size:
- 656.63 KB
- Format:
- Adobe Portable Document Format
- Description:
- Full printable version