Classification via local manifold approximation
成果类型：
Article
署名作者：
Li, Didong; Dunson, David B.
署名单位：
Duke University; Duke University
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asaa033
发表日期：
2020
页码：
10131020
关键词：
摘要：
Classifiers label data as belonging to one of a set of groups based on input features. It is challenging to achieve accurate classification when the feature distributions in the different classes are complex, with nonlinear, overlapping and intersecting supports. This is particularly true when training data are limited. To address this problem, we propose a new type of classifier based on obtaining a local approximation to the support of the data within each class in a neighbourhood of the feature to be classified, and assigning the feature to the class having the closest support. This general algorithm is referred to as local manifold approximation classification. As a simple and theoretically supported special case, which is shown to have excellent performance across a broad variety of examples, we use spheres for local approximation, obtaining a spherical approximation classifier.