Abstract
AbstractSince its introduction in the 1990s, non-negative matrix factorization (NMF) has captured a great amount of attention due to its capability and effectiveness in processing data in such a way that few earlier methods could perform, partly due to its non-negative constraint. This paper first briefly presents the basic NMF algorithm and concerns with the algorithm itself, then demonstrates its power with three applications in three different fields, namely face recognition in Computer Vision, distance prediction in Networking and molecular pattern discovery in Genetics. The paper ends with a quick look at other applications of NMF and recent developments that researchers have made.KeywordsNon-negative matrix factorization applicationsFace recognitionNetwork distance predictionMolecular pattern discovery
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.