Adaptive Semi-supervised Marginal Fisher Analysis
Haifeng Sui · 2011
Graph based semi-supervised methods have successfully used in face recognition.These algorithms not only consider the label information,but also utilize a consistency assumption.Conventional algorithms assumed that the consistency constraint is defined on the original feature space.However,the original feature space is not the best for defining consistency.We proposed adaptive semi-supervised marginal fisher analysis(ASMFA) by which the consistency constraint is defined in the original feature space and the expected low-dimensional feature space.Experimental results on the CMU PIE and YALE-B databases demonstrate that ASMFA brings signification improvement in face recognition accuracy.