Comparative Assessment of Dimensionality Reduction Techniques for Multivariate Analysis of Coral Bleaching Thermal Stress
Resumo
This paper presents a comparative analysis of seven dimensionality reduction (DR) techniques applied to the BCO-DMO global coral bleaching dataset, investigating multivariate patterns of oceanic thermal stress from 1998 to 2019. Linear Discriminant Analysis proved most effective for supervised discriminant visualization of ocean basin structure, outperforming unsupervised techniques in revealing class-defined thermal patterns within this dataset. Temporal projections reveal progressive homogenization of thermal stress across major open ocean basins and an anomalous regime shift in the Arabian Gulf, with a 30% increase in maximum thermal stress between 1998–2004 and 2012–2019. These findings highlight the potential of supervised DR as a diagnostic tool for analyzing complex environmental data through an open-source web platform that supports interactive and reproducible exploration.Referências
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Montambault, B., Appleby, G., Rogers, J., Brumar, C. D., Li, M. and Chang, R. (2024) “DimBridge: Interactive Explanation of Visual Patterns in Dimensionality Reductions with Predicate Logic”, IEEE Transactions on Visualization and Computer Graphics, 31(1), p. 207-217.
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Schölkopf, B., Smola, A. and Müller, K.-R. (1998) “Nonlinear component analysis as a kernel eigenvalue problem”, Neural Computation, 10(5), p. 1299-1319.
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Van der Maaten, L. and Hinton, G. (2008) “Visualizing data using t-SNE”, Journal of Machine Learning Research, 9(11).
Van Woesik, R. and Kratochwill, C. (2022) “A global coral-bleaching database, 1980–2020”, Scientific Data, 9, p. 20. DOI: 10.1038/s41597-022-01121-y.
Chaidez, V., Dreano, D., Agusti, S., Duarte, C. M. and Hoteit, I. (2017) “Decadal trends in Red Sea maximum surface temperature”, Scientific Reports, 7(1), p. 8144.
Davies, D. L. and Bouldin, D. W. (1979) “A cluster separation measure”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), p. 224-227.
Goldberger, J., Roweis, S., Hinton, G. and Salakhutdinov, R. (2004) “Neighbourhood components analysis”, Advances in Neural Information Processing Systems, 17.
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Hassani, H., Huang, X. and Silva, E. (2019) “Big data and climate change”, Big Data and Cognitive Computing, 3(1), p. 12.
Hughes, T. P., Kerry, J. T., Álvarez-Noriega, M., Álvarez-Romero, J. G., Anderson, K. D., Baird, A. H., Babcock, R. C., Beger, M., Bellwood, D. R., Berkelmans, R. et al. (2018) “Spatial and temporal patterns of mass bleaching of corals in the Anthropocene”, Science, 359(6371), p. 80-83.
Jeon, H. et al. (2025) “Unveiling high-dimensional backstage: a survey for reliable visual analytics with dimensionality reduction”, In: CHI Conference on Human Factors in Computing Systems, ACM, New York, p. 1-24.
Jolliffe, I. T. and Cadima, J. (2016) “Principal component analysis: a review and recent developments”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), p. 20150202.
Lopes, M. A. S., Dória Neto, A. D. and Martins, A. M. (2020) “Parallel t-SNE Applied to Data Visualization in Smart Cities”, IEEE Access, 8, p. 11482-11490.
Martinez, A. M. and Kak, A. C. (2001) “PCA versus LDA”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), p. 228-233.
Montambault, B., Appleby, G., Rogers, J., Brumar, C. D., Li, M. and Chang, R. (2024) “DimBridge: Interactive Explanation of Visual Patterns in Dimensionality Reductions with Predicate Logic”, IEEE Transactions on Visualization and Computer Graphics, 31(1), p. 207-217.
Rousseeuw, P. J. (1987) “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis”, Journal of Computational and Applied Mathematics, 20, p. 53-65.
Schölkopf, B., Smola, A. and Müller, K.-R. (1998) “Nonlinear component analysis as a kernel eigenvalue problem”, Neural Computation, 10(5), p. 1299-1319.
Shah, S. and Joshi, S. (2021) “Study of various dimensionality reduction and classification algorithms on high dimensional dataset”, In: International Conference on Inventive Research in Computing Applications, IEEE, Coimbatore, p. 1005-1010.
Tenenbaum, J. B., Silva, V. and Langford, J. C. (2000) “A global geometric framework for nonlinear dimensionality reduction”, Science, 290(5500), p. 2319-2323.
Van der Maaten, L. and Hinton, G. (2008) “Visualizing data using t-SNE”, Journal of Machine Learning Research, 9(11).
Van Woesik, R. and Kratochwill, C. (2022) “A global coral-bleaching database, 1980–2020”, Scientific Data, 9, p. 20. DOI: 10.1038/s41597-022-01121-y.
Publicado
19/07/2026
Como Citar
CASADO, Luana D. P. E.; LIRA, Ingrid G. C.; GOMES, João V. da C.; LOPES, Maximiliano A. da S.; CASTRO, Angélica F. de.
Comparative Assessment of Dimensionality Reduction Techniques for Multivariate Analysis of Coral Bleaching Thermal Stress. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 53. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 215-226.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2026.22523.
