Extracting Information from Interval Data Using Symbolic Principal Component Analysis

Austrian Journal of Statistics

article

M. R. Oliveira¹  M. Vilela¹  A. Pacheco¹  Rui Valadas²  Paulo Salvador³ 

[1] CEMAT and Instituto Superior Técnico, Universidade de Lisboa;IT and Instituto Superior Técnico, Universidade de Lisboa;IT andUniversidade de Aveiro

关键词: interval data;    symbolic principal component analysis;    Internet data;   

DOI  :  10.17713/ajs.v46i3-4.673

学科分类：医学（综合）

来源: Austrian Statistical Society

【 摘 要 】

We introduce generic definitions of symbolic variance and covariance for random interval-valued variables, that lead to a unified and insightful interpretation of four known symbolic principal component estimation methods: CPCA, VPCA, CIPCA, and SymCovPCA. Moreover, we propose the use of truncated versions of symbolic principal components, that use a strict subset of the original symbolic variables, as a way to improve the interpretation of symbolic principal components. Furthermore, the analysis of a real dataset leads to a meaningful characterization of Internet traffic applications, while highligting similarities between the symbolic principal component estimation methods considered in the paper.

【 授权许可】

CC BY