Abstract A manifold learning algorithm is proposed called discriminant maximum margin criterion (DMMC). It adopts linear projective maps and optimally preserves the local structure and the global information of the data set simultaneously. DMMC tries to find the intrinsic manifold that discriminates different face classes best by maximizing the between-class scatter and minimizing the within-class scatter. The recognition rate of the proposed algorithm exceeds those of the single PCA,Fisherfaces,MMC and LPP greatly. Experimental results on YALE and JAFFE face databases indicate that the proposed algorithm is effective.
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2010, Vol. 23 
