DocumentCode
507627
Title
Supervised Discriminant Nonnegative Matrix Factorization Method
Author
Pei, XiaoBing ; Xiao, Laiyuan
Author_Institution
Coll. of Software, Huazhong Univ. of Sci. & Technol., Wuhan, China
Volume
1
fYear
2009
fDate
Nov. 30 2009-Dec. 1 2009
Firstpage
172
Lastpage
174
Abstract
In this paper, a supervised discriminant NMF (SDNMF) model is investigated. The idea is to incorporate the discriminate and the class information preserving constraints into the NMF decomposition in order to extract latent semantic spaces that enforce the discriminate and class information preserving properties. Finally, experimental evaluation is performed on the SECTOR data set.
Keywords
constraint handling; document handling; matrix decomposition; NMF decomposition; SECTOR data set; class information preserving constraints; discriminate information preserving properties; latent semantic spaces; supervised discriminant nonnegative matrix factorization method; Additives; Biomedical imaging; Data mining; Educational institutions; Knowledge acquisition; Large scale integration; Matrix decomposition; Principal component analysis; Space technology; Sparse matrices; Dimensionality reduction; Nonnegative matrix factorization;
fLanguage
English
Publisher
ieee
Conference_Titel
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3888-4
Type
conf
DOI
10.1109/KAM.2009.261
Filename
5362247
Link To Document