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, C., Martella, F., Vichi, M.. - In: MULTIVARIATE BEHAVIORAL RESEARCH. - ISSN 0027-3171. - STAMPA. - 54(1):(2019), pp. 47-61. [10.1080/00273171.2018.1485006]
Probabilistic Disjoint Principal Component Analysis
Ferrara Carla
;Martella Francesca;Vichi Maurizio
2019
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.| File | Dimensione | Formato | |
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