Grand Tours, Projection Pursuit Guided Tours, and Manual Controls
Grand Tours, Projection Pursuit Guided Tours, and Manual Controls
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大型游览、投影追踪导览和手动控制
DOI：
10.1007/978-3-540-33037-0_13
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
H. Wickham
中科院分区：
文献类型：
--
作者：
D. Cook;A. Buja;Eun;H. Wickham
当计算机屏幕只有二维时，我们如何在多维数据中找到结构？一种方法是将数据投影到一维或二维上。投影用于经典的统计方法，如主成分分析（PCA）和线性判别分析。PCA（例如，约翰逊和Wichern 2002）选择一个投影来最大化方差。Fisher线性判别（例如，约翰逊和Wichern 2002）选择一个投影，最大化组平均值之间的相对分离。投影寻踪（PP）（例如，Huber 1985）将这些思想推广到一个通用策略中，其中优化了投影上的任意函数。散点图矩阵（例如，Becker和Cleveland（1987）也可以被认为是一种投影方法。它显示数据在所有坐标轴对上的投影，即数据的2-D边缘投影。这些投影方法从无限多个投影中选择一些选定的投影。
How do we find structure in multidimensional data when computer screens are only two-dimensional? One approach is to project the data onto one or two dimensions. Projections are used in classical statistical methods like principal component analysis (PCA) and linear discriminant analysis. PCA (e.g., Johnson and Wichern 2002) chooses a projection to maximize the variance. Fisher’s linear discriminant (e.g., Johnson and Wichern 2002) chooses a projection that maximizes the relative separation between group means. Projection pursuit (PP) (e.g., Huber 1985) generalizes these ideas into a common strategy, where an arbitrary function on projections is optimized. The scatterplot matrix (e.g., Becker and Cleveland 1987) also can be considered to be a projection method. It shows projections of the data onto all pairs of coordinate axes, the 2-D marginal projections of the data. These projection methods choose a few select projections out of infinitely many.