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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Official URL: https://doi.org/10.1016/j.patcog.2018.06.014
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: | Schools > Mathematics Professional Services > 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: | 18 Jan 2025 22:30 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/112507 |
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