Name
Abstract | ||
|---|---|---|
In this work we take a novel view of nonlinear manifold learning. Usually, manifold learning is formulated in terms of finding an embedding or 'unrolling' of a manifold into a lower dimensional space. Instead, we treat it as the problem of learning a representation of a nonlinear, possibly non-isometric manifold that allows for the manipulation of novel points. Central to this view of manifold learning is the concept of generalization beyond the training data. Drawing on concepts from supervised learning, we establish a framework for studying the problems of model assessment, model complexity, and model selection for manifold learning. We present an extension of a recent algorithm, Locally Smooth Manifold Learning (LSML), and show it has good generalization properties. LSML learns a representation of a manifold or family of related manifolds and can be used for computing geodesic distances, finding the projection of a point onto a manifold, recovering a manifold from points corrupted by noise, generating novel points on a manifold, and more. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1273496.1273527 | ICML |
Keywords | Field | DocType |
manifold learning,related manifold,model assessment,non-isometric manifold learning,novel view,non-isometric manifold,nonlinear manifold learning,model complexity,supervised learning,novel point,model selection,geodesic distance | Semi-supervised learning,Computer science,Manifold alignment,Hilbert manifold,Artificial intelligence,Statistical manifold,Nonlinear dimensionality reduction,Atlas (topology),Local tangent space alignment,Pattern recognition,Algorithm,Simplicial manifold,Machine learning | Conference |
Citations | PageRank | References |
41 | 1.47 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Piotr Dollár | 1 | 7999 | 307.07 |
| Vincent Rabaud | 2 | 270 | 14.91 |
| Serge J. Belongie | 3 | 12512 | 1010.13 |