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2013 (Vol. 6, Issue: 09)
Article Information:

Linear Projective Non-Negative Matrix Factorization

Lirui Hu, Jianguo Wu and Lei Wang
Corresponding Author:  Lirui Hu 

Key words:  Face recognition, linear transformation, non-negative matrix factorization, projective, , ,
Vol. 6 , (09): 1626-1631
Submitted Accepted Published
December 23, 2012 January 25, 2013 July 15, 2013

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.
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  Cite this Reference:
Lirui Hu, Jianguo Wu and Lei Wang, 2013. Linear Projective Non-Negative Matrix Factorization.  Research Journal of Applied Sciences, Engineering and Technology, 6(09): 1626-1631.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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