Topological Data Analysis

Larry Wasserman

doi:10.1146/annurev-statistics-031017-100045

2018-03-07

291

PRE

Larry Wasserman *

DOI：10.1146/annurev-statistics-031017-100045

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摘要

En 中文

Topological data analysis (TDA) can broadly be described as a collection of data analysis methods that find structure in data. These methods include clustering, manifold estimation, nonlinear dimension reduction, mode estimation, ridge estimation and persistent homology. This paper reviews some of these methods.

Keyword:

clustering

persistent homology

dimension reduction

manifold estimation

density trees

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对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Annual Review of Statistics and Its Application

IF:

8.7

论文数:

211

被引数:

2.4K

机构

Carnegie Mellon University

学者数:

1.4W

论文数: 1.4W

被引数: 2.7W

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