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Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

Sara Sharifzadeh Orcid Logo, Ali Ghodsi, Line H. Clemmensen, Bjarne K. Ersbøll

Engineering Applications of Artificial Intelligence, Volume: 65, Pages: 168 - 177

Swansea University Author: Sara Sharifzadeh Orcid Logo

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DOI (Published version): 10.1016/j.engappai.2017.07.004

Published in: Engineering Applications of Artificial Intelligence
ISSN: 0952-1976
Published: Elsevier BV 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa65607
first_indexed 2024-04-04T11:45:57Z
last_indexed 2024-11-25T14:16:27Z
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spelling 2024-04-04T12:46:44.0688280 v2 65607 2024-02-09 Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2024-02-09 MACS Journal Article Engineering Applications of Artificial Intelligence 65 168 177 Elsevier BV 0952-1976 Variable selection; Dimension reduction; Sparse PCA; Supervised PCA; Sparse supervised PCA; Penalized matrix decomposition 1 10 2017 2017-10-01 10.1016/j.engappai.2017.07.004 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 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. 2024-04-04T12:46:44.0688280 2024-02-09T01:16:42.5473395 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Sara Sharifzadeh 0000-0003-4621-2917 1 Ali Ghodsi 2 Line H. Clemmensen 3 Bjarne K. Ersbøll 4
title Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection
spellingShingle Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection
Sara Sharifzadeh
title_short Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection
title_full Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection
title_fullStr Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection
title_full_unstemmed Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection
title_sort Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection
author_id_str_mv a4e15f304398ecee3f28c7faec69c1b0
author_id_fullname_str_mv a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh
author Sara Sharifzadeh
author2 Sara Sharifzadeh
Ali Ghodsi
Line H. Clemmensen
Bjarne K. Ersbøll
format Journal article
container_title Engineering Applications of Artificial Intelligence
container_volume 65
container_start_page 168
publishDate 2017
institution Swansea University
issn 0952-1976
doi_str_mv 10.1016/j.engappai.2017.07.004
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 0
active_str 0
published_date 2017-10-01T08:28:05Z
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