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Data visualization by nonlinear dimensionality reduction
Author(s) -
Gisbrecht Andrej,
Hammer Barbara
Publication year - 2015
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1147
Subject(s) - dimensionality reduction , computer science , visualization , cluster analysis , reduction (mathematics) , data mining , curse of dimensionality , nonlinear dimensionality reduction , data visualization , focus (optics) , big data , machine learning , creative visualization , data science , artificial intelligence , mathematics , physics , geometry , optics
In this overview, commonly used dimensionality reduction techniques for data visualization and their properties are reviewed. Thereby, the focus lies on an intuitive understanding of the underlying mathematical principles rather than detailed algorithmic pipelines. Important mathematical properties of the technologies are summarized in the tabular form. The behavior of representative techniques is demonstrated for three benchmarks, followed by a short discussion on how to quantitatively evaluate these mappings. In addition, three currently active research topics are addressed: how to devise dimensionality reduction techniques for complex non‐vectorial data sets, how to easily shape dimensionality reduction techniques according to the users preferences, and how to device models that are suited for big data sets. WIREs Data Mining Knowl Discov 2015, 5:51–73. doi: 10.1002/widm.1147 This article is categorized under: Technologies > Machine Learning Technologies > Structure Discovery and Clustering Technologies > Visualization