Large‐scale data visualization with missing values

Large‐scale data visualization with missing values

Visualization of large‐scale data inherently requires dimensionality reduction to 1D, 2D, or 3D space. Autoassociative neural networks with a bottleneck layer are commonly used as a nonlinear dimensionality reduction technique. However, many real‐world problems suffer from incomplete data sets, i.e....

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Bibliographic Details
Main Author: Sergiy Popov
Format: Article
Language:English
Published: Vilnius Gediminas Technical University 2006-03-01
Series:Technological and Economic Development of Economy
Subjects:
data visualization
dimensionality reduction
autoassociative neural network
network training
missing values
incomplete data set
Online Access:http://www.mla.vgtu.lt/index.php/TEDE/article/view/7967

Internet

http://www.mla.vgtu.lt/index.php/TEDE/article/view/7967

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