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Dimensionality Reduction for Data Visualization [Applications Corner]

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2 Author(s)

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.

Published in:

Signal Processing Magazine, IEEE  (Volume:28 ,  Issue: 2 )