Application of Multivariable Linear Regression in NMF Face Recognition

doi:10.3969/j.issn.1006-2475.2017.11.008

Abstract

Abstract: Single sub-optimal nonnegative basis features usually contain limited face category information and the recognition rate lies on the related low dimensional representation. On account of the weak classification of NMF, more basis features were proposed to develop more latent correlated category information by looking at the face recognition process of NMF carefully. And then, the multivariable linear regression methods were used to build label mapping from ensemble weak labels to true label. It integrated the weak correlated category structure information and rose the correct category structure to the surface well. The results on several face databases show that the statistical label mapping enhances the face recognition capability of NMF.

Key words: nonnegative matrix factorization, ensemble classification, partial least squares regression, ridge regression, face recognition

CLC Number:

TP391.1

GAO Liang1, PAN Ji-yuan2, YU Jia-ping3. Application of Multivariable Linear Regression in NMF Face Recognition[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.11.008.

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