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
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9172
Deposited By:Samuel Kaski
Deposited On:21 February 2012