Visual Component Analysis

Müller, Wolfgang; Alexa, Marc

Visual Component Analysis

Files

129-136.pdf (366.72 KB)

Date

2004

Authors

Müller, Wolfgang
Alexa, Marc

Publisher

The Eurographics Association

Abstract

We propose to integrate information visualization techniques with factor analysis. Specifically, a principal direction derived from a principal component analysis (PCA) of the data is displayed together with the data in a scatterplot matrix. The direction can be adjusted to coincide with visual trends in the data. Projecting the data onto the orthogonal subspace allows determining the next direction. The set of directions identified in this way forms an orthogonal space, which represents most of the variation in the data. We call this process visual component analysis (VCA). Furthermore, it is quite simple to integrate VCA with clustering. The user fits poly-lines to the displayed data, and the poly-lines implicitly define clusters. Per-cluster projection leads to the definition of per-cluster components.

        @inproceedings{10.2312:VisSym/VisSym04/129-136
,
booktitle = {Eurographics / IEEE VGTC Symposium on Visualization
},
editor = {Oliver Deussen and Charles Hansen and Daniel Keim and Dietmar Saupe
},
title = {{Visual Component Analysis
}},
author = {Müller, Wolfgang and 
Alexa, Marc
},
year = {2004
},
publisher = {The Eurographics Association
},
ISSN = {1727-5296
},
ISBN = {3-905673-07-X
},
DOI = {10.2312/VisSym/VisSym04/129-136
}
}