Dimensionality Reduction problem: a Comprehensive Exploration of Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA)
This paper delves into the realm of advanced data analysis, focusing on two powerful dimensionality reduction methods: Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA). Methodological marvels in their own right, these approaches are scrutinized for th...
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| Format: | Article |
| Language: | English |
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Milano University Press
2024-04-01
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| Series: | Epidemiology, Biostatistics and Public Health |
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| Online Access: | https://riviste.unimi.it/index.php/ebph/article/view/22513 |
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| author | Mario Fordellone |
| author_facet | Mario Fordellone |
| author_sort | Mario Fordellone |
| collection | DOAJ |
| description | This paper delves into the realm of advanced data analysis, focusing on two powerful dimensionality reduction methods: Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA). Methodological marvels in their own right, these approaches are scrutinized for their unique properties and applications across diverse domains. We navigate through the intricacies of their algorithms and explore how they unveil patterns within complex datasets. The comparative analysis highlights the strengths and weaknesses of DPCA and DMCA, shedding light on their distinct contributions to the analytical landscape. This paper serves as a comprehensive guide for researchers and analysts seeking deeper insights into these cutting-edge techniques for dimensional reduction.
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| format | Article |
| id | doaj-art-64b790f5d2d94f8389cb7d3b10e7bfab |
| institution | DOAJ |
| issn | 2282-0930 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Milano University Press |
| record_format | Article |
| series | Epidemiology, Biostatistics and Public Health |
| spelling | doaj-art-64b790f5d2d94f8389cb7d3b10e7bfab2025-08-20T03:17:32ZengMilano University PressEpidemiology, Biostatistics and Public Health2282-09302024-04-0118210.54103/2282-0930/22513Dimensionality Reduction problem: a Comprehensive Exploration of Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA)Mario Fordellone0https://orcid.org/0000-0003-0790-892XUniversità degli Studi della Campania Luigi VanvitelliThis paper delves into the realm of advanced data analysis, focusing on two powerful dimensionality reduction methods: Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA). Methodological marvels in their own right, these approaches are scrutinized for their unique properties and applications across diverse domains. We navigate through the intricacies of their algorithms and explore how they unveil patterns within complex datasets. The comparative analysis highlights the strengths and weaknesses of DPCA and DMCA, shedding light on their distinct contributions to the analytical landscape. This paper serves as a comprehensive guide for researchers and analysts seeking deeper insights into these cutting-edge techniques for dimensional reduction. https://riviste.unimi.it/index.php/ebph/article/view/22513dimensionality reduction modelmultivariate analysisprincipal component analysismultiple corrispondence analysis |
| spellingShingle | Mario Fordellone Dimensionality Reduction problem: a Comprehensive Exploration of Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA) Epidemiology, Biostatistics and Public Health dimensionality reduction model multivariate analysis principal component analysis multiple corrispondence analysis |
| title | Dimensionality Reduction problem: a Comprehensive Exploration of Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA) |
| title_full | Dimensionality Reduction problem: a Comprehensive Exploration of Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA) |
| title_fullStr | Dimensionality Reduction problem: a Comprehensive Exploration of Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA) |
| title_full_unstemmed | Dimensionality Reduction problem: a Comprehensive Exploration of Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA) |
| title_short | Dimensionality Reduction problem: a Comprehensive Exploration of Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA) |
| title_sort | dimensionality reduction problem a comprehensive exploration of disjoint principal component analysis dpca and disjoint multiple correspondence analysis dmca |
| topic | dimensionality reduction model multivariate analysis principal component analysis multiple corrispondence analysis |
| url | https://riviste.unimi.it/index.php/ebph/article/view/22513 |
| work_keys_str_mv | AT mariofordellone dimensionalityreductionproblemacomprehensiveexplorationofdisjointprincipalcomponentanalysisdpcaanddisjointmultiplecorrespondenceanalysisdmca |