关键词: 数据降维, 流形学习, 测地距离, 等距离映射算法, 局部线性嵌入

Abstract: The TRIMAP algorithm redefines the expression of  the distance on the graph, and in order to measure the quality of the projection functions, considers the squared error sum of all pair wise geodesic. This way can better find what is needed from high-dimensional space to low-dimensional vector space conversion. But this measure can’t be well express the contrast relationship between graph distance which is defined in TRIMAP algorithm and actual distance which is projected to low dimensional space. Aiming at this deficiency, this paper uses a new standard expression and defines a parameter m to represent relationship in order to solve the defect, get the best projection and improve the recognition rate. The preliminary experimental results show that it can get a better recognition performance in the ORL face image classification and recognition problem.

Key words: data dimension reduction, manifold learning, geodesic distance, ISOMAP algorithm, locally linear embedding