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...

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Vydané v:	IEEE transactions on visualization and computer graphics Ročník 26; číslo 1; s. 822 - 831
Hlavní autori:	Gortler, Jochen, Spinner, Thilo, Streeb, Dirk, Weiskopf, Daniel, Deussen, Oliver
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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