Checking the projection display of multivariate data with colored graphs

被引:19
作者
Bienfait, B [1 ]
Gasteiger, J [1 ]
机构
[1] Univ Erlangen Nurnberg, Inst Organ Chem, Comp Chem Ctr, D-91052 Erlangen, Germany
关键词
principal component analysis; PCA; nonlinear mapping; NLM; self-organizing map; SOM; Kohonen's network; minimal spanning tree; MST; k-largest distortions graph; k-LDG; interatomic distances; histogram; frequency distribution; autocorrelation; QSAR;
D O I
10.1016/S0263-7855(97)00078-7
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Projection methods such as principal component analysis (PCA), nonlinear mapping (NLM), and the self-organizing map (SOM) are valuable algorithms for visualizing multi dimensional data in a two-dimensional plane. Unfortunately, the reduction of the dimensionality involves distortions. In all attempt to graphically localize the distortions of the projected data, we suggest superposing colored graphs onto the 2D plots. The color of the edges of these graphs encodes the original high-dimensional distances between the connected points. The method is applied to a cluster analysis of 37 biologically active compounds and 471 molecules represented by a structural 3D descriptor. (C) 1998 by Elsevier Science Inc.
引用
收藏
页码:203 / +
页数:18
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