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Assessing similarities between spatial point patterns with a Siamese neural network discriminant model

Author

Listed:
  • Abdollah Jalilian

    (Razi University)

  • Jorge Mateu

    (Universitat Jaume I)

Abstract

Identifying structural differences among observed point patterns from several populations is of interest in several applications. We use deep convolutional neural networks and employ a Siamese framework to build a discriminant model for distinguishing structural differences between spatial point patterns. In a simulation study, and using a one-shot learning classification, we show that the Siamese network discriminant model outperforms the common dissimilarities based on intensity and K functions. The model is then used to analyze similarities between spatial point patterns of 130 species in a tropical rainforest study plot observed at different time instances. The simulation study and data analysis show the adequacy and generality of a Siamese network discriminant model in the classification of spatial point patterns.

Suggested Citation

  • Abdollah Jalilian & Jorge Mateu, 2023. "Assessing similarities between spatial point patterns with a Siamese neural network discriminant model," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 21-42, March.
    • Handle: RePEc:spr:advdac:v:17:y:2023:i:1:d:10.1007_s11634-021-00485-0
    DOI: 10.1007/s11634-021-00485-0
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    References listed on IDEAS

    as
    1. Abdollah Jalilian, 2017. "Modelling and classification of species abundance: a case study in the Barro Colorado Island plot," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2401-2409, October.
    2. Jean-François Coeurjolly & Jesper Møller & Rasmus Waagepetersen, 2017. "A Tutorial on Palm Distributions for Spatial Point Processes," International Statistical Review, International Statistical Institute, vol. 85(3), pages 404-420, December.
    3. Avner Bar-Hen & Nicolas Picard, 2006. "Simulation study of dissimilarity between point process," Computational Statistics, Springer, vol. 21(3), pages 487-507, December.
    4. Jalilian, Abdollah, 2016. "On the higher order product density functions of a Neyman–Scott cluster point process," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 144-150.
    5. Cholaquidis, Alejandro & Forzani, Liliana & Llop, Pamela & Moreno, Leonardo, 2017. "On the classification problem for Poisson point processes," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 1-15.
    6. David J. Williamson & Garth L. Burn & Sabrina Simoncelli & Juliette Griffié & Ruby Peters & Daniel M. Davis & Dylan M. Owen, 2020. "Machine learning for cluster analysis of localization microscopy data," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    7. Rasmus Waagepetersen & Yongtao Guan & Abdollah Jalilian & Jorge Mateu, 2016. "Analysis of multispecies point patterns by using multivariate log-Gaussian Cox processes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(1), pages 77-96, January.
    Full references (including those not matched with items on IDEAS)

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