CAJ | 학술논문

最大方差差分嵌入算法（VDE）最大化全局方差和局部方差之差，该算法直接通过求解一个特征值问题而获得投影矩阵，无需矩阵求逆运算，因此VDE克服了无监督鉴别投影（UDP）算法的小样本问题，为了进一步增强VDE算法的非线性描述能力，提出了核最大方差差分嵌入算法（KVDE），该算法首先采用核函数将样本映射到非线性高维空间，然后采用核方法得到一个低维子空间，人脸和掌纹数据库上的实验表明KVDE算法比VDE算法具有更好的性能。
최대방차차분감입산법（VDE）최대화전국방차화국부방차지차，해산법직접통과구해일개특정치문제이획득투영구진，무수구진구역운산，인차VDE극복료무감독감별투영（UDP）산법적소양본문제，위료진일보증강VDE산법적비선성묘술능력，제출료핵최대방차차분감입산법（KVDE），해산법수선채용핵함수장양본영사도비선성고유공간，연후채용핵방법득도일개저유자공간，인검화장문수거고상적실험표명KVDE산법비VDE산법구유경호적성능。
Maximum Variance Difference Embedding(VDE)maximizes the difference between the global variance and the local variance, which utilizes the maximum variance difference criterion rather than the generalized Rayleigh quotient criterion as a class separability measure, thereby avoiding the singularity problem of Unsupervised Discriminant Projec-tion(UDP)when addressing the sample size problem. However, VDE is a linear approach, which cannot describe the complex nonlinear data, such as biometric data. To enhance the nonlinear description ability of VDE, it can optimize the objective function of VDE in reproducing kernel Hilbert space to construct Kernel-based maximum Variance Difference Embedding(KVDE) approach. Compared with some other related classical methods, experimental results on face and palmprint recognition problems indicate the effectiveness of the proposed KVDE.