PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dimensionality reduction for data visualization
Samuel Kaski and Jaakko Peltonen
IEEE Signal Processing Magazine Volume 28, Number 2, pp. 100-104, 2011.

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 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.

EPrint Type:Article
Additional Information:http://dx.doi.org/10.1109/MSP.2010.940003
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:9068
Deposited By:Jaakko Peltonen
Deposited On:21 February 2012