摘要
针对LLE算法在数据密度变化较大时很难降维的问题,提出一种基于密度刻画的降维算法。采用cam分布寻找数据点的近邻,并在低维局部重建时对数据点加入密度信息。对手写体数字图像进行字符特征的降维,再对降维后的特征进行分类识别。实验结果表明,该方法能区分字符,具有较好的识别率,能够发现高维空间的低维嵌入流形。
In order to improve the correctness of dimensionality reduction algorithms based on Locally Linear Embedding(LLE) caused by data density change,a novel approach based on density is proposed in this paper.It adapts cam distribute to find the data’s nearest neighbor,meanwhile,adds the data’s density information during the low dimensional local reconstruction.The proposed algorithm is used to reduce the dimensionality of input feature,and the reduced feature is classified by simple classifier.Experimental result indicates that the method can effectively improve the recognition rate of handwritten digits and can dig the manifold embedded in the high dimensional space.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第21期138-140,共3页
Computer Engineering
基金
辽宁省教育厅高等学校科学研究基金资助项目(2008344)
中国科学院自动化研究所复杂系统与智能科学重点实验室开放课题基金资助项目(20070101)
关键词
流形学习
降维
密度信息
手写体识别
cam分布
manifold learning
dimensionality reduction
density information
handwriting recognition
cam distribution