Evaluating Autoencoders as a Dimensionality Reduction Mechanism to Support Clustering Brazilian Agricultural Diversity

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Marcos Aurélio Santos da Silva
https://orcid.org/0000-0002-5367-2869
Leonardo Nogueira Matos
https://orcid.org/0000-0002-6302-3299
Gastão Florêncio Miranda Júnior
https://orcid.org/0000-0002-0967-6141
Flávio Emanuel de Oliveira Santos
https://orcid.org/0000-0002-7041-5581
Márcia Helena Galina Dompieri
https://orcid.org/0000-0001-7689-1602
Fábio Rodrigues de Moura
https://orcid.org/0000-0002-6532-110X
Fabrícia Karollyne Santos Resende
https://orcid.org/0000-0001-8010-6304

Resumo

Brazilian agricultural production presents high spatial diversity, challenging the conception of public policies. This article proposes an approach for grouping Brazilian municipalities according to their agricultural production. We combine a feature extraction using autoencoders and clustering based on k-means and Self-Organizing Maps. We used panel data from IBGE’s annual estimates of the production value of permanent and temporary crops, animal products, aquaculture, plant extractivism, forestry, planted areas, and herd population between 1999 and 2018. We analyzed different structures of simple stacked and incomplete autoencoders, varying the number of layers and neurons in each, and evaluated the asymmetric exponential linear loss function to handle the sparse data. We applied the Isomap, Kernel PCA, Truncated SVD, and MDS dimensionality reduction methods for comparative purposes. Results showed that the autoencoders could extract characteristics from the transformed raw data to allow the clustering of municipalities to reveal regional and even
intra-regional patterns. The autoencoders improved comparative performance as the intrinsic dimensionality increased.

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SILVA, M. A. S. da; MATOS, L. N.; MIRANDA JÚNIOR, G. F.; SANTOS, F. E. de O.; DOMPIERI, M. H. G.; MOURA, F. R. de; RESENDE, F. K. S. Evaluating Autoencoders as a Dimensionality Reduction Mechanism to Support Clustering Brazilian Agricultural Diversity. Revista Brasileira de Cartografia, [S. l.], v. 75, 2023. DOI: 10.14393/rbcv75n0a-68733. Disponível em: https://seer.ufu.br/index.php/revistabrasileiracartografia/article/view/68733. Acesso em: 18 jul. 2024.
Seção
Seção Especial "Brazilian Symposium on GeoInformatics - GEOINFO 2023"

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