Semi-supervised local diversity graph embedding algorithm
Liang Xing-zh · Transducer and Microsystem Technologies · 2014
Aiming at the shortcomings of semi-supervised algorithm based on graph embedding,a novel method called semi-supervised local diversity graph embedding algorithm( SLDGEA) is proposed.The idea of this algorithm preserves the local structure and simultaneously maximizes the diversity of data,SLDGEA can avoid the data over-learning problem.In order to solve small sample problem,SLDGEA adopts differential form of the criterion function,which can adjust samples effect of the two parts through parameters.Experimental results on ORL and UMIST face databases demonstrate that SLDGEA is better than the existing two kinds of classical algorithms and the optimal recognition rates are improved by 2.25 % and 2.23 %.