Learning to Traverse Image Manifolds
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Open Access Publications from the University of California

Learning to Traverse Image Manifolds


We present a new algorithm, Locally Smooth Manifold Learning (LSML), that learns a warping function from a point on an manifold to its neighbors. Important characteristics of LSML include the ability to recover the structure of the manifold in sparsely populated regions and beyond the support of the provided data. Applications of our proposed technique include embedding with a natural out-of-sample extension and tasks such as tangent distance estimation, frame rate up-conversion, video compression and motion transfer.

Pre-2018 CSE ID: CS2007-0876

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