PDF(Pubmed)
Abstract：
Correspondence analysis (CA) is a multivariate statistical and visualization technique. CA is extremely useful in analyzing either two- or multi-way contingency tables, representing some degree of correspondence between columns and rows. The CA results are visualized in easy-to-interpret \"bi-plots,\" where the proximity of items (values of categorical variables) represents the degree of association between presented items. In other words, items positioned near each other are more associated than those located farther away. Each bi-plot has two dimensions, named during the analysis. The naming of dimensions adds a qualitative aspect to the analysis. Correspondence analysis may support medical professionals in finding answers to many important questions related to health, wellbeing, quality of life, and similar topics in a simpler but more informal way than by using more complex statistical or machine learning approaches. In that way, it can be used for dimension reduction and data simplification, clustering, classification, feature selection, knowledge extraction, visualization of adverse effects, or pattern detection.
摘要：
对应分析（CA）是一种多元统计和可视化技术。CA在分析双向或多路列联表时非常有用，表示列和行之间的一定程度的对应关系。CA结果以易于解释的“双图”可视化，其中项目的接近度（分类变量的值）表示所呈现项目之间的关联程度。换句话说,彼此靠近的项目比距离更远的项目更相关。每个双图都有两个维度，在分析过程中命名。维度的命名为分析增加了定性方面。对应分析可以支持医疗专业人员找到与健康有关的许多重要问题的答案，幸福,生活质量,与使用更复杂的统计或机器学习方法相比，以更简单但更非正式的方式进行类似主题。这样,它可以用于降维和数据简化，聚类，分类，特征选择,知识提取，不利影响的可视化，或模式检测。
