A parsimonious parameterization of a nonnegative correlation matrix

Carlo Cavicchia, Maurizio Vichi, Giorgia Zaccaria*

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review


Hierarchical relationships among manifest variables can be detected by
analyzing their correlation matrix. To pinpoint the hierarchy underlying a multidimensional phenomenon, the Ultrametric Correlation Model (UCM) has been proposed with the aim of reconstructing a nonnegative correlation matrix via an ultrametric one. In this paper, we illustrate the mathematical advantages that a simple structure induced by the ultrametric property entails for the estimation of the UCM parameters in a maximum likelihood framework.
Original languageEnglish
Title of host publicationBook of Short Papers of the 5th international workshop on Models and Learning for Clustering and Classification MBC2 2020, Catania, Italy
EditorsSalvatore Ingrassia, Antonio Punzo, Roberto Rocci
Publication statusPublished - 2021


Dive into the research topics of 'A parsimonious parameterization of a nonnegative correlation matrix'. Together they form a unique fingerprint.

Cite this