项目介绍

drewwilimitis/Manifold-Learning 是一个托管在 GitHub 上的开源项目,主要使用 Jupyter Notebook 编写,目前获得 259 个 Star、39 次 Fork。

仓库全名
drewwilimitis/Manifold-Learning
Star 数
259
Fork 数
39
未关闭 Issue
5

README 摘要

Manifold Learning: Introduction and Foundational Algorithms Mathematical Theory with Examples and Applications in Python Contents Introduction: Overview of manifolds and the basic topology of data Statistical learning and instrinsic dimensionality The manifold hypothesis Chapter 1: Multidimensional Scaling Classical, metric, and non-metric MDS algorithms Example applications to quantitative psychology and social…

完整内容见 GitHub 原始 README 或本页 。

常见问题

Manifold-Learning 用什么语言开发?

主要使用 Jupyter Notebook 编写,目前有 259 个 Star。

如何获取 Manifold-Learning 的源码?

可以直接克隆仓库:git clone https://github.com/drewwilimitis/Manifold-Learning.git;安装与运行步骤见仓库 README。

Manifold-Learning 最近还在维护吗?

仓库最近一次更新为 2020年3月5日,当前有 5 个未关闭 Issue;Star 数为 259,可作为社区活跃度的参考。

关于

Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

描述取自 GitHub 官方 API,中文内容由 GitRead 中文社区 编辑团队整理。

README

Manifold Learning: Introduction and Foundational Algorithms

Mathematical Theory with Examples and Applications in Python

KleinDual


Contents

  • Introduction:

    • Overview of manifolds and the basic topology of data
    • Statistical learning and instrinsic dimensionality
    • The manifold hypothesis
  • Chapter 1: Multidimensional Scaling

    • Classical, metric, and non-metric MDS algorithms
    • Example applications to quantitative psychology and social science
  • Chapter 2: ISOMAP

    • Geodesic distances and the isometric mapping algorithm
    • Implementation details and applications with facial images and coil-100 object images
  • Chapter 3: Local Linear Embedding

    • Locally linear reconstructions and optimization problems
    • Example applications with image data
  • Chapter 4: Laplacian Eigenmaps/Spectral Embedding

    • From the general to the discrete Laplacian operators
    • Visualizing spectral embedding with the networkx library
    • Spectral embedding with NLTK and the Brown text corpus

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