论文标题
多维持久性模块通过格子理论卷积分类
Multidimensional Persistence Module Classification via Lattice-Theoretic Convolutions
论文作者
论文摘要
多参数持续的同源性在很大程度上被忽略为机器学习算法的输入。我们考虑使用基于晶格的卷积神经网络层作为分析多参数持续模块引起的特征的工具。我们发现，这些表现有望作为多维持久模块分类的卷积的替代方法。
Multiparameter persistent homology has been largely neglected as an input to machine learning algorithms. We consider the use of lattice-based convolutional neural network layers as a tool for the analysis of features arising from multiparameter persistence modules. We find that these show promise as an alternative to convolutions for the classification of multidimensional persistence modules.
