ICML 2011 workshop on unsupervised
and transfer learning
Supervised
Dimensionality Reduction in the Unsupervised and Transfer Learning 2011
Competition
Yann-Aël
Le Borgne (Tryan team, Fourth place in second ranking of phase 2 UTL
challenge),
VUB, Belgium
This paper discusses
preliminary results in the use of a supervised dimensionality reduction
strategy to tackle the 2011 Unsupervised and Transfer Learning
Competition. The basis of the strategy is to make use of the three
subsets provided for each dataset in order to assign labels to
examples. Using those labels, we rely on the partial least square
technique to find a linear transformation that extract features of
interest. We investigated two approaches. The first only uses three
classes, assigned to the development, validation, and final subsets.
The second additionally makes use of the transfer labels provided in
the second phase of the competition. Our results show that the
extracted features improve the classification accuracy upon the
baseline results for four of the six datasets. Although these results
are still preliminary, we believe that the proposed strategy provides
a flexible framework in which the performance could be greatly
increased.