heucoder / Dimensionality_reduction_alo_codes
Licence: apache-2.0
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 |  |
| KPCA | 资料链接1 资料链接2 资料链接3 | KPCA |  |
| LDA | 资料链接１ 资料链接2 | LDA |  |
| MDS | 资料链接１ | MDS |
 
|
| ISOMAP | 资料链接１ 资料链接２ | ISOMAP |  |
| LLE | 资料链接１ 资料链接2 | LLE |  |
| TSNE | 资料链接１ | TSNE |  |
| AutoEncoder | 无 |  |
|
| 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 |  |
| LPP | 资料链接１ 资料链接２ | 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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