One of the most relevant problems in Principal Component Analysis and Factor Analysis is the interpretation of the components/factors. In this paper, Disjoint Principal Component Analysis model is extended in a maximum likelihood framework to allow for inference on the model parameters. A coordinate ascent algorithm is proposed to estimate the model parameters. The performance of the methodology is evaluated on simulated and real data sets.
Probabilistic Disjoint Principal Component Analysis / Ferrara, Carla; Martella, Francesca; Vichi, Maurizio. - In: MULTIVARIATE BEHAVIORAL RESEARCH. - ISSN 0027-3171. - STAMPA. - (2018). [10.1080/00273171.2018.1485006]
Probabilistic Disjoint Principal Component Analysis
Ferrara Carla
;Martella Francesca;Vichi Maurizio
2018
Abstract
One of the most relevant problems in Principal Component Analysis and Factor Analysis is the interpretation of the components/factors. In this paper, Disjoint Principal Component Analysis model is extended in a maximum likelihood framework to allow for inference on the model parameters. A coordinate ascent algorithm is proposed to estimate the model parameters. The performance of the methodology is evaluated on simulated and real data sets.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
