Face Recognition Based on Metric-optimized Neighborhood Preserving Embedding
Hengyi Sun, Yangyu Fan, Jinhuan Wen, Meng Jia · Jisuanji gongcheng · 2011
Euclidean metric is adopted to look for k-nearest neighbors in the supervised Neighborhood Preserving Embedding(NPE).However,the results are not very good when Euclidean metric is directly generalized to handle high-dimensional data as dealing with low-dimensional data.To overcome this problem a metric-optimized neighborhood preserving embedding algorithm is proposed in this paper.Two conditions are considered: non-labeled case(MONPE) and labeled case(CLMONPE).The main idea is to choose k-nearest neighbors by analyzing the data whose dimension is reduced with linear discriminant analysis algorithm.Test result on Yale database shows that CLMONPE has obvious strength in application.