Open AccessApplication of Data Classification Method Based on Non-Negative Matrix Factorization
Author(s) -
Ling Jiao Li,
Ying Lin Liu,
Hong Huang,
Xiao Ping Fan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012120
Subject(s) - matrix decomposition , dimensionality reduction , non negative matrix factorization , curse of dimensionality , matrix (chemical analysis) , factorization , computer science , pattern recognition (psychology) , reduction (mathematics) , decomposition , artificial intelligence , feature extraction , representation (politics) , algorithm , mathematics , ecology , eigenvalues and eigenvectors , physics , materials science , geometry , composite material , quantum mechanics , politics , political science , law , biology
At present, there are many methods for data dimensionality reduction, but most of the results of decomposition methods allow negative values. Obviously, these negative values have no physical meaning in practical problems. The non-negative matrix factorization method is a dimensionality reduction under the condition of ensuring non-negative values. This paper mainly uses a non-negative matrix factorization algorithm to perform dimensionality reduction representation and local feature extraction of data, and then classify. Experiments show that the algorithm in this paper is reasonable and effective.