Towards Semi-supervised Manifold Learning: UKR with Structural Hints
Jan Steffen, Stefan Klanke, Sethu Vijayakumar and Helge Ritter
In: WSOM 2009, Orlando, Florida, USA(2009).
We explore generic mechanisms to introduce structural hints
into the method of Unsupervised Kernel Regression (UKR) in order to learn representations of data sequences in a semi-supervised way. These
new extensions are targeted at representing a dextrous manipulation
task. We thus evaluate the effectiveness of the proposed mechanisms on
appropriate toy data that mimic the characteristics of the aimed manipulation
task and thereby provide means for a systematic evaluation.