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heucoder/dimensionality_reduction_alo_codes

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heucoder/dimensionality_reduction_alo_codes has 2,371 GitHub stars and 607 forks. GitHot measured 0 stars in 24 hours. Last checked 2026-11-07 21:48 UTC. Last push 2023-05-10.

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特征提取/数据降维:PCA、LDA、MDS、LLE、TSNE等降维算法的python实现

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DimensionalityReduction_alo_codes

网上关于各种降维算法的资料参差不齐，同时大部分不提供源代码；在此通过借鉴资料实现了一些经典降维算法的Demo(python)，同时也给出了参考资料的链接。

降维算法 资料链接 代码 展示
PCA 资料链接１ 资料链接２ 资料链接３ PCA PCA
KPCA 资料链接1 资料链接2 资料链接3 KPCA KPCA
LDA 资料链接１ 资料链接2 LDA LDA
MDS 资料链接１ MDS MDS Tensor-MDS
ISOMAP 资料链接１ 资料链接２ ISOMAP ISOMAP
LLE 资料链接１ 资料链接2 LLE LLE
TSNE 资料链接１ TSNE TSNE
AutoEncoder 无 AutoEncoder
FastICA 资料链接１ FastICA
SVD 资料链接１ 资料链接2 SVD
LE 资料链接1资料链接2 LE LE
LPP 资料链接１ 资料链接２ LPP LPP

环境: python3.6 ubuntu18.04(windows10) 需要的库: numpy sklearn tensorflow matplotlib

  • 每一个代码都可以单独运行，但是只是作为一个demo，仅供学习使用
  • 其中AutoEncoder只是使用AutoEncoder简单的实现了一个PCA降维算法,自编码器涉及到了深度学习领域，其本身就是一个非常大领域
  • LE算法的鲁棒性极差，对近邻的选择和数据分布十分敏感
  • 2019.6.20添加了LPP算法，但是效果没有论文上那么好，有点迷，后续需要修改

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