Multi-scale local shape analysis and feature selection in machine learning applications
Multi-scale local shape analysis and feature selection in machine learning applications
复制标题
机器学习应用中的多尺度局部形状分析和特征选择
DOI：
10.1109/ijcnn.2015.7280428
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Linda Ness
中科院分区：
文献类型：
--
作者：
Paul Bendich;Ellen Gasparovic;J. Harer;R. Izmailov;Linda Ness
我们引入了一种称为多尺度局部形状分析的方法，用于提取描述数据集中点的局部结构的特征。该方法使用多个粒度级别的几何和拓扑特征来捕获不同类型的本地信息，以用于在数据集上操作的后续机器学习算法。使用合成和真实的数据集的例子，我们证明了显着的性能改进的分类算法，这些数据集与相应的增强功能。
We introduce a method called multi-scale local shape analysis for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse types of local information for subsequent machine learning algorithms operating on the dataset. Using synthetic and real dataset examples, we demonstrate significant performance improvement of classification algorithms constructed for these datasets with correspondingly augmented features.