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Poster
Nonlinear Learning using Local Coordinate Coding
Kai Yu · Tong Zhang · Yihong Gong

Tue Dec 08 07:00 PM -- 11:59 PM (PST) @ None #None
in Poster Session »

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.

Author Information

Kai Yu (Baidu)
Tong Zhang (Tencent)
Yihong Gong

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