Learning Transformations for Classification Forests

Qiang Qiu, Guillermo Sapiro

Dec 20, 2013 (modified: Dec 20, 2013) ICLR 2014 conference submission readers: everyone
  • Decision: submitted, no decision
  • Abstract: This work introduces a transformation-based learner model for classification forests. The weak learner at each split node plays a crucial role in a classification tree. We propose to optimize the splitting objective by learning a linear transformation on subspaces using nuclear norm as the optimization criteria. The learned linear transformation restores a low-rank structure for data from the same class, and, at the same time, maximizes the separation between different classes, thereby improving the performance of the split function. Theoretical and experimental results support the proposed framework.