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

Le cours se tiendra les mercredis matin de 9h à 12h15 sur 8 semaines à Paris
Santé Campus. Les dates s’étalent du 02/10 (inclus) au 20/11 (inclus):

02/10 : 9h-12h15
09/10 : 9h-12h15
16/10 : 9h-12h15
04/11 : 9h-12h15
06/11 : 9h-12h15
13/11 : 9h-12h15
18/11 : 9h-12h15
20/11 : 9h-12h15