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  1. http://www.cse.wustl.edu/~kilian/code/code.html На сайте исходники алгоритмов: 1) Large Margin Nearest Neighbors; 2) Maximum Variance Unfolding; 3) Gradient Boosted Regression Trees; 4) Random Forests; 5) marginalized Stacked Denoising Autoencoder; 6) Metric learning for kernel regression; 7) Learning with Marginalized Corrupted Features; 8) Co-training for domain adaptation; 9) Pseudo Multi-View Co-Training; 10) Fast Flux Descriminant Features. Ну и тапки http://tapkee.lisitsyn.me/ Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) Neighborhood Preserving Embedding (NPE) Local Tangent Space Alignment (LTSA) Linear Local Tangent Space Alignment (LLTSA) Hessian Locally Linear Embedding (HLLE) Laplacian eigenmaps Locality Preserving Projections Diffusion map Isomap and landmark Isomap Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) Stochastic Proximity Embedding (SPE) PCA and randomized PCA Kernel PCA (kPCA) Random projection Factor analysis t-SNE Barnes-Hut-SNE