Manifold Learning and Its Applications: Papers from the AAAI Fall Symposium (FS-10-06)
Preface: Manifold Learning and Its Applications
Oluwasanmi Koyejo and Richard Souvenir
Researchers in many fields such as machine learning, computer vision, bioinformatics and robotics often observe that
high dimensional data samples have low degrees of freedom
in local neighborhoods, but a more complicated global structure. In many cases, there is enough structure in the data so
the degrees of freedom can be described by a lower dimensional object such as a manifold.
The goal of manifold learning research is to discover techniques that exploit local structure in data to learn better models, learn better input-output relationships and reduce the
computational complexity of learning.
The field of manifold learning is truly cross-disciplinary,
involving researchers from such varied fields as topology,
geometry, machine learning, statistics, computer vision,
robotics and many others. This has led to an accelerating
pace of research and applications in recent years.
The goal of the symposium is to promote and discuss research developments in manifold learning, research on related approaches and applications to novel problems.
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