Tree-like Dimensionality Reduction for Cancer-informatics

2nd International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Zhang, Xia^1 ; Chang, Di^2 ; Qi, Weimin^1 ; Zhan, Zhiming^1

School of Physics and Information Engineering, Jianghan University, Wuhan

430056, China^1

Department of Computer Science, University of Georgia, Athens

GA

30602, United States^2

关键词: Dimensionality reduction;    Gene sequences;    Maximum spanning tree;    Mutual informations;    Sub fields;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/490/4/042028/pdf
DOI  :  10.1088/1757-899X/490/4/042028

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Dimensionality reduction in machine learning currently has become a very heated research filed. Traditional dimensionality reduction can be separated into two sub-fields of feature selection and feature extraction, but both of them are under local consideration. In this paper, an algorithm based on information theory, mutual information and maximum spanning tree will be proposed, in order to implement dimensionality reduction under global consideration rather than local consideration. Experimental results show it has a good performance, when the proposed algorithm is applied on gene sequences about cancer bioinformatics.