Neural networks with functional inputs for multi-class supervised classification of replicated point patterns

Kateřina Pawlasová (), Iva Karafiátová () and Jiří Dvořák ()
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Kateřina Pawlasová: Charles University
Iva Karafiátová: Charles University
Jiří Dvořák: Charles University

Advances in Data Analysis and Classification, 2024, vol. 18, issue 3, No 8, 705-721

Abstract: Abstract A spatial point pattern is a collection of points observed in a bounded region of the Euclidean plane or space. With the dynamic development of modern imaging methods, large datasets of point patterns are available representing for example sub-cellular location patterns for human proteins or large forest populations. The main goal of this paper is to show the possibility of solving the supervised multi-class classification task for this particular type of complex data via functional neural networks. To predict the class membership for a newly observed point pattern, we compute an empirical estimate of a selected functional characteristic. Then, we consider such estimated function to be a functional variable entering the network. In a simulation study, we show that the neural network approach outperforms the kernel regression classifier that we consider a benchmark method in the point pattern setting. We also analyse a real dataset of point patterns of intramembranous particles and illustrate the practical applicability of the proposed method.

Keywords: Functional data analysis; Pair correlation function; Spatial point patterns; Thomas point process; 60G55; 62R10; 68T07 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11634-024-00579-5

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