Semi-supervised Discriminant Analysis Based on Maximum Scatter Difference
Yan Wang · Journal of Information and Computational Science · 2013
For the problems of use of a few marked samples and small sample size problem in face recognition, a new algorithm of semi-supervised discriminant analysis based on maximum scatter difference is proposed. Based on MFA algorithm, the algorithm was improved semi-supervised by adding UDP local and non local scatter matrix to the target function. This could make use of the not marked samples that supervised algorithm wasted and the classification label information which unsupervised algorithm does not use. At the same time this solves to the problem that can not direct matrix inversion for scatter matrix singular by introducing the maximum scatter difference. Finally, the effectiveness of the proposed methods is validated through the experimental results on ORL and YALE face databases.