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Optimum scale selection for 3D point cloud classification through distance correlation. (English) Zbl 1444.62166
Aneiros, Germán (ed.) et al., Functional and high-dimensional statistics and related fields. Selected papers presented at the 5th international workshop on functional and operatorial statistics, IWFOS 2021, Brno, Czech Republic, June 23–25, 2021. Cham: Springer. Contrib. Stat., 213-220 (2020).
Summary: Multiple scale machine learning algorithms using handcrafted features are among the most efficient methods for 3D point cloud supervised classification and segmentation. Despite their proven good performance, there are still some aspects that are not fully solved, determining optimum scales being one of them. In this work, we analyze the usefulness of functional distance correlation to address this problem. Specifically, we propose to adjust functions to the distance correlation between each of the features, at different scales, and the labels of the points, and select as optimum scales those corresponding to the global maximum of said functions. The method, which to the best of our knowledge has been proposed in this context for the first time, was applied to a benchmark dataset and the results analyzed and compared with those obtained using other methods for scale selection.
For the entire collection see [Zbl 1446.62001].
62R10 Functional data analysis
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62H20 Measures of association (correlation, canonical correlation, etc.)
68T05 Learning and adaptive systems in artificial intelligence
Full Text: DOI
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