Data Visualization Method Based on MLE and Manifold Learning
Chuancai Liu · Jisuanji gongcheng · 2011
The method is stemmed 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 be 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.