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The problem of projecting multidimensional data into lower dimensions has been pursued by many researchers due to its potential application to data analysis of various kinds. This paper presents a novel multidimensional projection technique based on least square approximations. The approximations compute the coordinates of a set of projected points based on the coordinates of a reduced number of control points with defined geometry. We name the technique Least Square Projections (LSP). From an initial projection of the control points, LSP defines the positioning of their neighboring points through a numerical solution that aims at preserving a similarity relationship between the points given by a metric in $mD$. In order to perform the projection, a small number of distance calculations is necessary and no repositioning of the points is required to obtain a final solution with satisfactory precision. The results show the capability of the technique to form groups of points by degree of similarity in $2D$. We illustrate that capability through its application to mapping collections of textual documents from varied sources, a strategic yet difficult application. LSP is faster and more accurate than other existing high quality methods, particularly where it was mostly tested, that is, for mapping text sets.
Multivariate visualization, Data and knowledge visualization, Information visualization

H. Levkowitz, F. V. Paulovich, R. Minghim and L. G. Nonato, "Least Square Projection: A Fast High-Precision Multidimensional Projection Technique and Its Application to Document Mapping," in IEEE Transactions on Visualization & Computer Graphics, vol. 14, no. , pp. 564-575, 2007.
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