شماره ركورد كنفرانس :
4057
عنوان مقاله :
Nonnegative Matrix Factorization via Joint Graph Laplacian Information
عنوان به زبان ديگر :
Nonnegative Matrix Factorization via Joint Graph Laplacian Information
پديدآورندگان :
Gadr gharebagh Sakineh gadr1358@gmail.com Department of Mathematics, Faculty of Science, Urmia University of Technology , Biglari Fahimeh beiglari.f@gmail.com Department of Mathematics, Payame Noor University, P.O.Box 19395-3697, Tehran , Foroutan Mohammadreza foroutan−mohammadreza@yahoo.com Department of Mathematics, Payame Noor University, P.O.Box 19395-3697, Tehran , Ebadian Ali ebadian.ali@gmail.com Department of Mathematics, Faculty of Science, Urmia University
كليدواژه :
Convergence of numerical methods , multiplicative update algorithms , nonnegative matrix factorization , optimization methods.
عنوان كنفرانس :
چهارمين كنفرانس بين المللي آناليز غير خطي و بهينه سازي
چكيده فارسي :
In this paper, we proposed non-negative matrix factorization methods via joint graph Laplacian for identifying differ-
entially expressed genes, in which manifold learning and the discriminative label information are incorporated into the traditional
nonnegative matrix factorization model to train the objective matrix. Non-negative matrix factorization has attracted much atten-
tion and been widely used in real-world applications.
چكيده لاتين :
In this paper, we proposed non-negative matrix factorization methods via joint graph Laplacian for identifying differ-
entially expressed genes, in which manifold learning and the discriminative label information are incorporated into the traditional
nonnegative matrix factorization model to train the objective matrix. Non-negative matrix factorization has attracted much atten-
tion and been widely used in real-world applications.
