Academic Journals Database
Disseminating quality controlled scientific knowledge

Linear Projective Non-Negative Matrix Factorization

ADD TO MY LIST
 
Author(s): Lirui Hu | Jianguo Wu | Lei Wang

Journal: Research Journal of Applied Sciences, Engineering and Technology
ISSN 2040-7459

Volume: 6;
Issue: 9;
Start page: 1626;
Date: 2013;
Original page

Keywords: Face recognition | linear transformation | non-negative matrix factorization | projective

ABSTRACT
In order to solve the problem that the basis matrix is usually not very sparse in Non-Negative Matrix Factorization (NMF), a method, called Linear Projective Non-Negative Matrix Factorization (LP-NMF), is proposed. In LP-NMF, from projection and linear transformation angle, an objective function of Frobenius norm is defined. The Taylor series expansion is used. An iterative algorithm for basis matrix and linear transformation matrix is derived and a proof of algorithm convergence is provided. Experimental results show that the algorithm is convergent; relative to Non-negative Matrix Factorization (NMF), the orthogonality and the sparseness of the basis matrix are better; in face recognition, there is higher recognition accuracy. The method for LP-NMF is effective.
Why do you need a reservation system?      Affiliate Program