计算机工程2011，Vol.37Issue(1)：4-6,3.DOI:10.3969/j.issn.1000-3428.2011.01.002
基于MLE与流形学习的数据可视化方法
Data Visualization Method Based on MLE and Manifold Learning
摘要
Abstract
The method is stenuned from the assumption that each data set is a probabilistic realization of an underlying multinomial distribution under a partition on sample space. With the MLE of model parameters, the underlying distribution of a data set can be approximated by a discretized probability distribution. With the generalized Fisher metric on multinomial manifold with boundary, the information divergence between underlying models can bo approximated by the corresponding divergence between estimated distributions, it provides the necessary element for unsupervised learning on information manifold. The natural separation of original data sets can be visualized when the dimension of reduced space is two or three.Experimental result shows that the method can be applied to visualization of big sample data sets or color image data sets.关键词
多项分布/最大似然估计/流形学习/数据可视化Key words
multinomial distribution/ maximum likelihood estimation/ manifold learning/ data visualization分类
信息技术与安全科学引用本文复制引用
邹健,刘传才..基于MLE与流形学习的数据可视化方法[J].计算机工程,2011,37(1):4-6,3.基金项目
国家自然科学基金资助项目(9082004) （9082004）
国家"863"计划基金资助项目(2006AA04Z238) （2006AA04Z238）
安徽自然科学基金资助项目(KJ2007B056) （KJ2007B056）
