### "High Dimensional Nonlinear Learning using Local Coordinate Coding" (Next Week at the Statistics Seminar)

*Attention conservation notice*: Only of interest if you (1)
care about statistical learning in high-dimensional spaces and (2) are in
Pittsburgh.

Since manifold learning has been on my mind this week, owing to trying to
teach it in data-mining, I am extra pleased by the
scheduling of this talk:

- "High Dimensional Nonlinear Learning using Local Coordinate Coding"
- Prof. Tong Zhang,
Rutgers University
*Abstract:* We present a new method for learning nonlinear functions
in high dimension using semisupervised learning. Our method 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 on a high dimensional manifold can be locally
approximated by a linear combination of its nearby anchor points, with the
linear weights offering 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. The empirical success of our
approach has been demonstrated in a recent pascal image classification
competition, where the top performance was achieved by an NEC system using this
idea.
- (Joint work with Kai Yu at NEC Lab America.)
*Time and place*: 4 pm on Monday, 12 October 2009, in Doherty Hall
310

As always, the seminar is free and open to the public.

Enigmas of Chance

Posted at October 08, 2009 15:01 | permanent link