摘要
局部线性嵌入算法(LLE)中常用欧氏距离度量样本间相似度.而对于图像等高维数据,欧氏距离不能准确体现样本间的相似程度.文中提出基于马氏距离度量的局部线性嵌入算法(MLLE).算法首先从现有样本中学习到一个马氏度量,然后在LLE算法的近邻选择、现有样本及新样本降维过程中用马氏度量作为相似性度量.将MLLE算法及其它典型的流形学习算法在ORL和USPS数据库上进行对比实验,结果表明MLLE算法具有良好的识别性能.
Euclidean distance is normally used to measure the similarity between samples in locally linear embedding algorithm （LLE）. But for some high dimensional data, such as images, Euclidean distance can not accurately reflect the similarity between samples. A Mahalanobis distance metric based locally linear embedding algorithm （MLLE） is proposed. Firstly, MLLE ascertains a Mahalanobis metric from the existing samples. Then, the Mahalanobis metric is used to choose neighborhoods and to reduce the dimensionality of the existing samples and the new samples. The comparison result of MLLE algorithm and some classical manifold based algorithms on ORL and USPS databases proves that MLLE algorithm is effective in recognizing images.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2012年第2期318-324,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金资助项目(No.60873038)
关键词
局部线性嵌入
流形学习
降维
图像识别
Locally Linear Embedding, Manifold Learning, Dimensionality Reduction, ImageRecognition
