【摘要】 通过向二维局部保持投影(2D-LPP)算法中引入类间约束和类标识信息,得到二维判别局部保持投影(2D-DLPP)算法,使它拥有更多的判别信息。但它却面临复杂的参数选择问题,这使得它在解决识别问题时受到限制。为解决此问题,构造无参数的相似矩阵,提出无参数的二维判别局部投影(无参数2D-DLPP)算法。在Yale和ORL人脸库上的仿真实验结果表明,该算法与二维判别局部保持投影(2D-DLPP)、二维局部保持投影法(2D-LPP)和二维线性判别分析法(2D-LDA)相比能够取得更高的识别率。更多还原

【Abstract】 By introducing between-class scatter constraint and label information into two-dimensional locality preserving projections(2D-LPP) algorithm, two-Dimensional Discriminant Locality Preserving Projections(2D-DLPP) has more discriminant power than 2D-LPP. However, 2D-DLPP is confronted with the difficulty of parameter selection, which limits its power on solving recognition problem. To solve this problem, by constructing parameter-less affinity matrix, an algorithm called parameter-less two-dimensional discriminant locality preserving projections(parameter-less 2D-DLPP)is proposed.The simulation results on Yale and ORL face database show that the method in this paper can get higher recognition rate than 2D-DLPP, 2D-LPP and 2D-LDA.更多还原