Dimension reduction and manifold learning

Au sujet de ce cours

Enseignant : Eddie AAMARI

SEMESTRE 2

Program

– Manifold hypothesis and intrinsic dimension(s)
– Multidimensional scaling
– Linear dimension reduction (random projections, principal component analysis)
– Non-linear spectral methods (kernel PCA, ISOMAP, MVU, Laplacian eigenmaps)
– Ad-hoc distance-preserving methods (diffusion maps, LLE)
– Probabilistic dimension reduction and clustering (SNE, UMAP)
– Neural network-based dimensionality reduction

Bibliography

– Ghojogh, B., M. Crowley, F. Karray, and A. Ghodsi (2023). Elements of dimensionality reduction and manifold learning
– Lee, J. A., M. Verleysen, et al. (2007). Nonlinear dimensionality reduction

Créneaux

The course will be held at Paris Santé Campus, over the months of October and November. Sessions are announced on the online calendar.