In this talk we propose to combine two powerful ideas, boosting and
manifold learning. On the one hand, we improve AdaBoost by
incorporating knowledge on the structure of the data into base
classifier design and selection. On the other hand, we use AdaBoost's
efficient learning mechanism to significantly improve supervised and
semi-supervised algorithms proposed in the context of manifold
learning. Beside the specific manifold-based penalization, the
resulting algorithm also accommodates the boosting of a large family
of regularized learning algorithms.