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Contrastive multiple correspondence analysis (cMCA): Using contrastive learning to identify latent subgroups in political parties
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6382-2752
Univ Taipei, Taiwan.
2023 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 18, no 7Article in journal (Refereed) Published
Abstract [en]

Scaling methods have long been utilized to simplify and cluster high-dimensional data. However, the general latent spaces across all predefined groups derived from these methods sometimes do not fall into researchers interest regarding specific patterns within groups. To tackle this issue, we adopt an emerging analysis approach called contrastive learning. We contribute to this growing field by extending its ideas to multiple correspondence analysis (MCA) in order to enable an analysis of data often encountered by social scientists-containing binary, ordinal, and nominal variables. We demonstrate the utility of contrastive MCA (cMCA) by analyzing two different surveys of voters in the U.S. and U.K. Our results suggest that, first, cMCA can identify substantively important dimensions and divisions among subgroups that are overlooked by traditional methods; second, for other cases, cMCA can derive latent traits that emphasize subgroups seen moderately in those derived by traditional methods.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE , 2023. Vol. 18, no 7
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:liu:diva-197572DOI: 10.1371/journal.pone.0287180ISI: 001027854200041PubMedID: 37428735OAI: oai:DiVA.org:liu-197572DiVA, id: diva2:1795848
Note

Funding Agencies|Taiwan National Science and Technology Council [110-2410-H-845 -030 -MY2]; Knut and Alice Wallenberg Foundation [KAW 2019.0024]

Available from: 2023-09-11 Created: 2023-09-11 Last updated: 2023-09-11

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