摘要
针对局部样条嵌入算法(Local spline embedding,LSE)存在样本外点学习和无监督模式学习问题,本文提出了一种新颖的正交局部样条判别投影算法(O-LSDP).该算法通过引入明确的线性映射关系,构建平移缩放模型,以及正交化特征子空间,从而使该算法能够应用于模式分类问题并显著改善了算法的分类识别能力.在标准人脸数据库和植物叶片数据库上的实验结果验证了该算法的有效性与可行性.
In order to circumvent the two major shortcomings of the original local spline embedding （LSE） algorithm, i.e., out-of-sample and unsupervised learning, we proposed a novel feature extraction algorithm called orthogonal local spline discriminant projection （O-LSDP）. By introducing an explicit linear mapping, constructing different translation and resealing models for different classes as well as orthogonality feature subspace, the O-LSDP not only inherits the advantages of LSE which uses local tangent space as a representation of the local geometry so as to preserve the locM structure, but also makes full use of class information and orthogonal subspace to significantly improve the discriminant power. Experimental results on standard face databases and plant leaf data set demonstrate the feasibility and effectiveness of the proposed algorithm.
出处
《自动化学报》
EI
CSCD
北大核心
2013年第12期2077-2089,共13页
Acta Automatica Sinica
基金
国家自然科学基金(61272333,61273302,61005010)
安徽省自然科学基金(1208085MF94,1208085MF98,1308085MF84)资助~~
关键词
局部样条嵌入
最大边缘准则
特征提取
流形学习
Local spline embedding （LSE）, maximum margin criterion, feature extraction, manifold learning
