Abstract
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.
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 language | English |
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| Title of host publication | Book of Short Papers of the 5th international workshop on Models and Learning for Clustering and Classification MBC2 2020, Catania, Italy |
| Editors | Salvatore Ingrassia, Antonio Punzo, Roberto Rocci |
| Pages | 21-26 |
| Publication status | Published - 2021 |