Virta, Joni and Artemiou, Andreas  ORCID: https://orcid.org/0000-0002-7501-4090
2023.
Poisson PCA for matrix count data.
Pattern Recognition
138
, 109401.
10.1016/j.patcog.2023.109401
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Abstract
We develop a dimension reduction framework for data consisting of matrices of counts. Our model is based on the assumption of existence of a small amount of independent normal latent variables that drive the dependency structure of the observed data, and can be seen as the exact discrete analogue of a contaminated low-rank matrix normal model. We derive estimators for the model parameters and establish their limiting normality. An extension of a recent proposal from the literature is used to estimate the latent dimension of the model. The method is shown to outperform both its vectorization-based competitors and matrix methods assuming the continuity of the data distribution in analysing simulated data and real world abundance data.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Mathematics |
| Subjects: | Q Science > QA Mathematics |
| Publisher: | Elsevier |
| ISSN: | 0031-3203 |
| Date of First Compliant Deposit: | 9 February 2023 |
| Date of Acceptance: | 5 February 2023 |
| Last Modified: | 18 May 2023 03:55 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/156567 |
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