Many relevant multidimensional phenomena are defined by nested latent concepts, which can be represented by a tree-structure supposing a hierarchical relationship among manifest variables. The root of the tree is a general concept which includes more specific ones. The aim of the paper is to reconstruct an observed data correlation matrix of manifest variables through an ultrametric correlation matrix which is able to pinpoint the hierarchical nature of the phenomenon under study. With this scope, we introduce a novel model which detects consistent latent concepts and their relationships starting from the observed correlation matrix.

中文翻译：

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用于分层潜在概念建模的超度量相关矩阵

嵌套潜在概念定义了许多相关的多维现象，这些潜在现象可以由假设清单变量之间具有层次关系的树结构表示。树的根是一个通用概念，其中包括更具体的概念。本文的目的是通过超度量相关矩阵重建清单变量的观测数据相关矩阵，该矩阵能够指出正在研究的现象的层次性质。在此范围内，我们引入了一个新颖的模型，该模型从观察到的相关矩阵开始检测一致的潜在概念及其关系。