Journal article 119 views
Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection
Engineering Applications of Artificial Intelligence, Volume: 65, Pages: 168 - 177
Swansea University Author:
Sara Sharifzadeh
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DOI (Published version): 10.1016/j.engappai.2017.07.004
Abstract
Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection
Published in: | Engineering Applications of Artificial Intelligence |
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ISSN: | 0952-1976 |
Published: |
Elsevier BV
2017
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Online Access: |
Check full text
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URI: | https://cronfa.swan.ac.uk/Record/cronfa65607 |
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Keywords: |
Variable selection; Dimension reduction; Sparse PCA; Supervised PCA; Sparse supervised PCA; Penalized matrix decomposition |
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College: |
Faculty of Science and Engineering |
Funders: |
This work was (in part) financed by the Centre for Imaging Food Quality project which is funded by the Danish Council for Strategic Research (contract No. 09-067039) within the Program Commission on Health, Food and Welfare. |
Start Page: |
168 |
End Page: |
177 |