Poisson PCA for matrix count data

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:	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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