Dimensionality Reduction problem: a Comprehensive Exploration of Disjoint Principal Component Analysis (DPCA) and Disjoint Multiple Correspondence Analysis (DMCA)
DOI:
https://doi.org/10.54103/2282-0930/22513Keywords:
dimensionality reduction model, multivariate analysis, principal component analysis, multiple corrispondence analysisAbstract
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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Copyright (c) 2023 Mario Fordellone
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Accepted 2024-03-19
Published 2024-04-18