Data embedding techniques.
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.
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