1. Introduction -- 2. Partial least squares -- 3. Canonical correlation analysis -- 4. Hypergraph spectral learning -- 5. A scalable two-stage approach for dimensionality reduction -- 6. A shared-subspace learning framework -- 7. Joint dimensionality reduction and classification -- 8. Nonlinear dimensionality reduction : algorithms and applications.