有监督图优化保局投影

摘要

This paper focuses on the construction and optimization of neighbour graph and proposes a Supervised Graph-optimized Locality Preserving Projections (SGoLPP) method for facial feature extraction. Different from the Locality Preserving Projections(LPP) that it predefines the weight matrix and solves the projection matrix by one step optimization,the SGoLPP incorporates the weight matrix into the objective function as a learning term, and optimizes the weight matrix and projection matrix simultaneously. Meanwhile, the label information is utilized to update the weights corresponding to sample pairs in the same class and to avoid the interferences from samples not in the same class. Experiments on the Wine database of UCI show that the SGoLPP achieves better cluster performance with less iterations. For face recognition, the average recognition accuracies of SGoLPP on Yale,UMIST and CMU PIE face databases are 26.6％, 4.8％ and 8.8％ higher than those of LPP, Supervised Locality Preserving Projections (SLPP) and Graph-optimized Locality Preserving Projections (GoLPP), respectively, which verifies the effectiveness and superiority of the proposed method.%研究了保局投影中近邻图的构造及更新问题,提出了一种有监督图优化保局投影(SGoLPP)特征提取方法,并应用于人脸识别.不同于传统的保局投影(LPP)算法预先设定权值矩阵并通过一次优化求解投影矩阵,SGoLPP将权值矩阵作为学习项引入到目标函数,通过交替迭代更新逐步获得最优权值矩阵和最优投影矩阵.同时,通过引入类别信息,始终对同类样本点对的权值进行更新,有效地抑制了异类样本的干扰.在UCI模拟数据集上,SGoLPP在较少的迭代次数下获得了更好的聚类和分类效果.在Yale,UMIST和CMU PIE人脸库上,SGoLPP的平均识别率比LPP、有监督保局投影(SLPP)和图优化保局投影(GoLPP)分别高出26.6%、4.8%和8.8%.实验显示本文提出的SGoLPP算法在样本可分性与鲁棒性方面具有优势,可有效地提取人脸特征.