多维标度
多维标度(Multidimensional scaling,缩写MDS,又译“多维尺度”)也称作“相似度结构分析”(Similarity structure analysis),属于多重变量分析的方法之一,是社会学、数量心理学、市场营销等统计实证分析的常用方法。
单词 | Multidimensional scaling |
释义 |
Multidimensional scaling
中文百科
多维标度多维标度(Multidimensional scaling,缩写MDS,又译“多维尺度”)也称作“相似度结构分析”(Similarity structure analysis),属于多重变量分析的方法之一,是社会学、数量心理学、市场营销等统计实证分析的常用方法。
英语百科
Multidimensional scaling 多维标度![]() ![]() ![]() Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset. It refers to a set of related ordination techniques used in information visualization, in particular to display the information contained in a distance matrix. An MDS algorithm aims to place each object in N-dimensional space such that the between-object distances are preserved as well as possible. Each object is then assigned coordinates in each of the N dimensions. The number of dimensions of an MDS plot N can exceed 2 and is specified a priori. Choosing N=2 optimizes the object locations for a two-dimensional scatterplot. |
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