Uncertainty-Aware Principal Component Analysis

We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to non-linear methods, linear dimensionality reduction techniqu...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on visualization and computer graphics Jg. 26; H. 1; S. 822 - 831
Hauptverfasser:	Gortler, Jochen, Spinner, Thilo, Streeb, Dirk, Weiskopf, Daniel, Deussen, Oliver
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Covariance matrices Covariance matrix Data visualization Dimensionality reduction linear projection machine learning Mathematical analysis Matrix methods Multivariate analysis Principal component analysis Principal components analysis Probability distribution Random variables Reduction Sensitivity analysis Uncertainty Uncertainty analysis
ISSN:	1077-2626, 1941-0506, 1941-0506
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!