Visual Component Analysis

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dc.contributor.authorMüller, Wolfgangen_US
dc.contributor.authorAlexa, Marcen_US
dc.contributor.editorOliver Deussen and Charles Hansen and Daniel Keim and Dietmar Saupeen_US
dc.date.accessioned2014-01-30T07:46:04Z
dc.date.available2014-01-30T07:46:04Z
dc.date.issued2004en_US
dc.description.abstractWe 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.en_US
dc.description.seriesinformationEurographics / IEEE VGTC Symposium on Visualizationen_US
dc.identifier.isbn3-905673-07-Xen_US
dc.identifier.issn1727-5296en_US
dc.identifier.urihttp://dx.doi.org/10.2312/VisSym/VisSym04/129-136en_US
dc.publisherThe Eurographics Associationen_US
dc.titleVisual Component Analysisen_US
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VisSym04: Joint Eurographics - IEEE TCVG Symposium on Visualization