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

Tue Dec 08 07:00 PM -- 11:59 PM (PST) @

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 (The Hong Kong University of Science and Technology)
Yihong Gong

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