Dimensionality reduction for data visualization
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||5|
|Journal||IEEE Signal Processing Magazine|
|Publication status||Published - Mar 2011|
|Publication type||A1 Journal article-refereed|
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 representing 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.