Premium
Visualizing singular value decomposition
Author(s) -
Zhang Lingsong,
Wang Yao
Publication year - 2014
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1295
Subject(s) - computer science , exploratory data analysis , visualization , principal component analysis , singular value decomposition , graphics , cluster analysis , data mining , statistical graphics , data visualization , sample (material) , multivariate statistics , computer graphics , the internet , pattern recognition (psychology) , artificial intelligence , machine learning , computer graphics (images) , chemistry , chromatography , world wide web
Singular value decomposition/principal component analysis method is a central tool in multivariate analysis and functional data analysis. In this article, a list of visualization tools that are useful in revealing structure within sample datasets is investigated. An Internet traffic dataset is used to illustrate the usefulness of these methods. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Algorithms and Computational Methods > Computer Graphics Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization