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Dimensionality reduction for data visualization

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Article number5714379
Pages (from-to)100-104
Number of pages5
JournalIEEE Signal Processing Magazine
Volume28
Issue number2
DOIs
Publication statusPublished - Mar 2011
Publication typeA1 Journal article-refereed

Abstract

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.

Keywords

  • Data models, Data visualization, Information retrieval, Machine learning, Manifolds, Probabilistic logic, Visualization