Variants of Unsupervised Kernel Regression: General cost functions

We present an extension to a recent method for learning of nonlinear manifolds, which allows to incorporate general cost functions. We focus on the -insensitive loss and visually demonstrate our method on both toy and real data.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Stefan Klanke, Helge Ritter
• Keywords: ESANN 2006, ESANN 2007, General Cost Functions, Nonlinear Manifolds