A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
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. some values can be missing. Common methods dealing with missing data include the deletion of all cases with missing values from the data set or replacement with mean or "normal" values for specificdoi:10.3846/13928619.2006.9637721 fatcat:jqtpeg7g3vdtbmxq4l3h7rllf4