Semi-supervised Optimal Locality Preserving Projection for Face Recognition
Yang Xiaomei · Science Technology and Engineering · 2013
To ignore non-supervised information of unlabeled samples is the main problem of recently proposed Supervised Optimal Locality Preserving Projection(SOLPP) in applications of face recognition.Aiming to the problem,a Semi-supervised Optimal Locality Preserving Projection(SSOLPP) for face recognition is proposed.On the base of SOLPP,the algorithm introduces Principal Component Analysis(PCA) with the weighted trade-off parameter way,making projected data preserve global scatter structure information and supervised optimal local structure information of high-dimensional data.Experimental results on Yale and YaleB demonstrate the effectiveness of our proposed algorithm.