Matlab数据降维工具箱drtoolbox

drtoolbox: Matlab Toolbox for Dimensionality Reduction是Laurens van der Maaten开发的用来数据降维的工具箱，其中包含了著名的PCA,LDA算法，流行学习算法MLE,LLE,LPP,SNE，Isomap等，和度量学习算法LMNN,MCML,NCA等。具体列表如下(摘自原始网站)

1. Principal Component Analysis (PCA)
2. Probabilistic PCA
3. Factor Analysis (FA)
4. Classical multidimensional scaling (MDS)
5. Sammon mapping
6. Linear Discriminant Analysis (LDA)
7. Isomap
8. Landmark Isomap
9. Local Linear Embedding (LLE)
10. Laplacian Eigenmaps
11. Hessian LLE
12. Local Tangent Space Alignment (LTSA)
13. Conformal Eigenmaps (extension of LLE)
14. Maximum Variance Unfolding (extension of LLE)
15. Landmark MVU (LandmarkMVU)
16. Fast Maximum Variance Unfolding (FastMVU)
17. Kernel PCA
18. Generalized Discriminant Analysis (GDA)
19. Diffusion maps
20. Neighborhood Preserving Embedding (NPE)
21. Locality Preserving Projection (LPP)
22. Linear Local Tangent Space Alignment (LLTSA)
23. Stochastic Proximity Embedding (SPE)
24. Deep autoencoders (using denoising autoencoder pretraining)
25. Local Linear Coordination (LLC)
26. Manifold charting
27. Coordinated Factor Analysis (CFA)
28. Gaussian Process Latent Variable Model (GPLVM)
29. Stochastic Neighbor Embedding (SNE)
30. Symmetric SNE
31. t-Distributed Stochastic Neighbor Embedding (t-SNE)
32. Neighborhood Components Analysis (NCA)
33. Maximally Collapsing Metric Learning (MCML)
34. Large-Margin Nearest Neighbor (LMNN)

这里提供两种下载方式：

1.原始网站下载http://lvdmaaten.github.io/drtoolbox/

2.本站下载http://download.csdn.net/detail/henryvivid/9719637

下载解压后将文件夹放到MATLABtoolbox中并且添加路径即可，使用代码很简单

[mapped_data, mapping] = compute_mapping(data, method, # of dimensions, parameters)

其中data是原始数据，每行是一个样本点，每列是一个特征，# of dimensions表示目标维度。

参考文献：

31.L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning 

Research 9(Nov):2579-2605, 2008.

32.Goldberger J, Hinton G E, Roweis S T, et al. Neighbourhood components analysis[C]//Advances in neural information 

processing systems. 2004: 513-520.

33.Globerson A, Roweis S T. Metric learning by collapsing classes[C]//Advances in neural information processing systems. 

2005: 451-458.

34.Weinberger K Q, Blitzer J, Saul L K. Distance metric learning for large margin nearest neighbor classification[C]//

Advances in neural information processing systems. 2005: 1473-1480.

35.L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. Dimensionality Reduction: A Comparative Review. 

Tilburg University Technical Report, TiCC-TR 2009-005, 2009.