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high-dimensional_data [2012/10/22 10:14]
melancon
high-dimensional_data [2015/10/22 09:58] (current)
melancon [Parallel coordinates]
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 |{{:​parallel_coordinates.png?​nolink&​100|}}| |{{:​parallel_coordinates.png?​nolink&​100|}}|
  
-Parallel coordinates offer an alternative to scatterplot matrix by allowing the simultaneous visualization of multiple variables of a same dataset using polylines. ​IN a nutshell: imagine the data has been laid out in an p-dimensional space, positioning an element e = (x_1, ..., x_p) at this precise position in Euclidean space. Now, project each of the unit axis onto parallel lines in the 2D planes. What you get is a parallel coordinates view of your data.+Parallel coordinates offer an alternative to scatterplot matrix by allowing the simultaneous visualization of multiple variables of a same dataset using polylines. ​In a nutshell: imagine the data has been laid out in an p-dimensional space, positioning an element e = (x_1, ..., x_p) at this precise position in Euclidean space. Now, project each of the unit axis onto parallel lines in the 2D planes. What you get is a parallel coordinates view of your data.
  
 Put differently,​ parallel coordinates view can be realized by using parallel vertical axis (positioned at x = i) each ranging over the range of the ith variable. For each element e = (x_1, ..., x_p) we draw a polyline crossing the ith axis at y = x_i.  Put differently,​ parallel coordinates view can be realized by using parallel vertical axis (positioned at x = i) each ranging over the range of the ith variable. For each element e = (x_1, ..., x_p) we draw a polyline crossing the ith axis at y = x_i. 
/net/html/perso/melancon/Visual_Analytics_Course/data/pages/high-dimensional_data.txt · Last modified: 2015/10/22 09:58 by melancon