Name
Abstract | ||
|---|---|---|
This paper introduces a new method for semi-supervised learning on high dimensional nonlinear manifolds, which includes a phase of unsupervised basis learning and a phase of supervised function learning. The learned bases provide a set of anchor points to form a local coordinate system, such that each data point x on the manifold can be locally approximated by a linear combination of its nearby anchor points, and the linear weights become its local coordinate coding. We show that a high dimensional nonlinear function can be approximated by a global linear function with respect to this coding scheme, and the approximation quality is ensured by the locality of such coding. The method turns a difficult nonlinear learning problem into a simple global linear learning problem, which overcomes some drawbacks of traditional local learning methods. |
Year | Venue | Field |
|---|---|---|
2009 | NIPS | Coordinate system,Linear combination,Locality,Mathematical optimization,Nonlinear system,Semi-supervised learning,Computer science,Coding (social sciences),Artificial intelligence,Linear function,Manifold,Machine learning |
DocType | Citations | PageRank |
Conference | 142 | 10.27 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu, Kai | 1 | 4799 | 255.21 |
| Zhang, Tong | 2 | 7126 | 611.43 |
| yihong gong | 3 | 7300 | 470.57 |