Simple Poisson PCA: An algorithm for (sparse) feature extraction with simultaneous dimension determination

Smallman, Luke ORCID: https://orcid.org/0000-0002-4114-310X, Underwood, Will and Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 2020. Simple Poisson PCA: An algorithm for (sparse) feature extraction with simultaneous dimension determination. Computational Statistics 35 , pp. 559-577. 10.1007/s00180-019-00903-0

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Abstract

Dimension reduction tools offer a popular approach to analysis of high-dimensional big data. In this paper, we propose an algorithm for sparse Principal Component Analysis for non-Gaussian data. Since our interest for the algorithm stems from applications in text data analysis we focus on the Poisson distribution which has been used extensively in analysing text data. In addition to sparsity our algorithm is able to effectively determine the desired number of principal components in the model (order determination). The good performance of our proposal is demonstrated with both synthetic and real data examples.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics Advanced Research Computing @ Cardiff (ARCCA)
Subjects:	Q Science > QA Mathematics
Publisher:	Springer Verlag (Germany)
ISSN:	0943-4062
Date of First Compliant Deposit:	20 May 2019
Date of Acceptance:	16 May 2019
Last Modified:	05 May 2023 07:53
URI:	https://orca.cardiff.ac.uk/id/eprint/122624

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