Sparse generalised principal component analysis

Smallman, Luke ORCID: https://orcid.org/0000-0002-4114-310X, Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 and Morgan, Jennifer ORCID: https://orcid.org/0000-0002-7025-0350 2018. Sparse generalised principal component analysis. Pattern Recognition 83 , pp. 443-455. 10.1016/j.patcog.2018.06.014

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Abstract

In this paper, we develop a sparse method for unsupervised dimension reduction for data from an exponential-family distribution. Our idea extends previous work on Generalised Principal Component Analysis by adding L1 and SCAD penalties to introduce sparsity. We demonstrate the significance and advantages of our method with synthetic and real data examples. We focus on the application to text data which is high-dimensional and non-Gaussian by nature and discuss the potential advantages of our methodology in achieving dimension reduction.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Mathematics Advanced Research Computing @ Cardiff (ARCCA)
Subjects:	Q Science > QA Mathematics
Publisher:	Elsevier
ISSN:	0031-3203
Date of First Compliant Deposit:	18 June 2018
Date of Acceptance:	15 June 2018
Last Modified:	21 Nov 2024 11:00
URI:	https://orca.cardiff.ac.uk/id/eprint/112507

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