sklearn.manifold#
Data embedding techniques.
User guide. See the Manifold learning section for further details.
| Classical multidimensional scaling (MDS). | |
| Isomap Embedding. | |
| Locally Linear Embedding. | |
| Multidimensional scaling. | |
| Spectral embedding for non-linear dimensionality reduction. | |
| T-distributed Stochastic Neighbor Embedding. | |
| Perform a Locally Linear Embedding analysis on the data. | |
| Compute multidimensional scaling using the SMACOF algorithm. | |
| Project the sample on the first eigenvectors of the graph Laplacian. | |
| Indicate to what extent the local structure is retained. |