Dimensionality reduction for data visualization [applications corner]
S Kaski, J Peltonen - IEEE signal processing magazine, 2011 - ieeexplore.ieee.org
IEEE signal processing magazine, 2011•ieeexplore.ieee.org
Dimensionality reduction is one of the basic operations in the toolbox of data analysts and
designers of machine learning and pattern recognition systems. Given a large set of
measured variables but few observations, an obvious idea is to reduce the degrees of
freedom in the measurements by rep resenting them with a smaller set of more" condensed"
variables. Another reason for reducing the dimensionality is to reduce computational load in
further processing. A third reason is visualization.
designers of machine learning and pattern recognition systems. Given a large set of
measured variables but few observations, an obvious idea is to reduce the degrees of
freedom in the measurements by rep resenting them with a smaller set of more" condensed"
variables. Another reason for reducing the dimensionality is to reduce computational load in
further processing. A third reason is visualization.
Dimensionality reduction is one of the basic operations in the toolbox of data analysts and designers of machine learning and pattern recognition systems. Given a large set of measured variables but few observations, an obvious idea is to reduce the degrees of freedom in the measurements by rep resenting them with a smaller set of more "condensed" variables. Another reason for reducing the dimensionality is to reduce computational load in further processing. A third reason is visualization.
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