Manifold Learning and Its Applications: Papers from the AAAI Fall Symposium
Oluwasanmi Koyejo and Richard Souvenir, Cochairs
Researchers and practitioners in many fields such as machine learning, computer vision, bioinformatics and robotics are increasingly faced with problems that require understanding and learning from high dimensional data. In such problems, manifold learning and related methods provide a compelling suite of techniques that can exploit local structure in data to learn better models, learn better input-output relationships and reduce the computational complexity of learning. The goal of the symposium is to promote and discuss research developments in manifold learning, research on related approaches and applications to novel problems.