The ever-growing amount of data stored in digital databases raises the question of how to organize and extract useful knowledge. This paper outlines some current developments in the domains of dimensionality reduction, manifold learning, and topological learning. Several aspects are dealt with, ranging from novel algorithmic approaches to their real world applications. The issue of quality assessment is also considered and progress in quantitive as well as visual criteria is reported.
['Axel Wismueller', 'Michel Verleysen', 'Michaël Aupetit', 'John Aldo Lee']
Recent Advances in Nonlinear Dimensionality Reduction, Manifold and Topological Learning