Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Zahra Moaberfard"'
Publikováno v:
Machine Learning with Applications, Vol 10, Iss , Pp 100440- (2022)
Low-rank matrix factorization problems such as non negative matrix factorization (NMF) can be categorized as a clustering or dimension reduction technique. The latter denotes techniques designed to find representations of some high dimensional datase
Externí odkaz:
https://doaj.org/article/943db972c2bb452bb5865e31f1de3a42
Low-rank matrix factorization problems such as non negative matrix factorization (NMF) can be categorized as a clustering or dimension reduction technique. The latter denotes techniques designed to find representations of some high dimensional datase
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::107cb0138a39aad9c401310ab826c2cb
https://doi.org/10.36227/techrxiv.12199026
https://doi.org/10.36227/techrxiv.12199026
Non-negative matrix factorization (NMF) has become a popular method for representing meaningful data by extracting a non-negative basis feature from an observed non-negative data matrix. Some of the unique features of this method in identifying hidde
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3baaeae52e0a8c44319a33cfb199689
Low-rank matrix factorization problems such as non negative matrix factorization (NMF) can be categorized as a clustering or dimension reduction technique. The latter denotes techniques designed to find representations of some high dimensional datase
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::016b21a0bc7e2440f8afd372084efe5b
https://doi.org/10.36227/techrxiv.12199026.v1
https://doi.org/10.36227/techrxiv.12199026.v1