Journal article 681 views
Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection
Sara Sharifzadeh 
,
Ali Ghodsi,
Line H. Clemmensen,
Bjarne K. Ersbøll
,
Ali Ghodsi,
Line H. Clemmensen,
Bjarne K. Ersbøll
Engineering Applications of Artificial Intelligence, Volume: 65, Pages: 168 - 177
Swansea University Author:
Sara Sharifzadeh
Full text not available from this repository: check for access using links below.
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 |
|---|---|
| ISSN: | 0952-1976 |
| Published: |
Elsevier BV
2017
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa65607 |
| Keywords: |
Variable selection; Dimension reduction; Sparse PCA; Supervised PCA; Sparse supervised PCA; Penalized matrix decomposition |
|---|---|
| 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 |