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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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dimensionality_reduction_alo_codes's Issues

LDA.py矩阵乘法使用错误

code

S = np.linalg.inv(Sw)*SB

Sw和SB都是ndarray,那么这里的乘号含义应该是矩阵各个位置相乘。如果我没有搞错的话,应该改为矩阵乘法(.dot)才对吧?

这样改的话,结果跟sklearn里边的也比较像。
图片

reconstruct版里边的实现和这个一样。我不确定仓库里是否有其他类似问题。

(写作业的时候发现对不上,研究了好久。说实话,numpy里边一种乘号还能有两种意义的,实在是令人震惊...)

About how to use myself datasets

Thank you very much for sharing your code. I want to use tSNE to visualize my dataset. How can I transform the dataset? In the tSNE example, the input is (n, d), and my image dataset is (n, c, h, w)

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