用于分类的持久性内核:比较研究

Cinzia Bandiziol, Stefano De Marchi
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引用次数: 0

摘要

本研究的目的是对应用于各种分类问题的不同持久性内核进行比较研究。在对同源性和持久性图做了一些必要的介绍后,我们引入了五种不同的核,然后用来比较它们在不同数据集上的分类性能。我们还提供了 Python 代码,以保证结果的可重复性。
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Persistence kernels for classification: A comparative study
The aim of the present work is a comparative study of different persistence kernels applied to various classification problems. After some necessary preliminaries on homology and persistence diagrams, we introduce five different kernels that are then used to compare their performances of classification on various datasets. We also provide the Python codes for the reproducibility of results.
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Tensor triangular geometry of modules over the mod 2 Steenrod algebra Ring operads and symmetric bimonoidal categories Inferring hyperuniformity from local structures via persistent homology Computing the homology of universal covers via effective homology and discrete vector fields Geometric representation of cohomology classes for the Lie groups Spin(7) and Spin(8)
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