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Learning transformations for classification forests

Publication ,  Conference
Qiu, Q; Sapiro, G
Published in: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings
January 1, 2014

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.

Duke Scholars

Published In

2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings

Publication Date

January 1, 2014
 

Citation

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Qiu, Q., & Sapiro, G. (2014). Learning transformations for classification forests. In 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings.
Qiu, Q., and G. Sapiro. “Learning transformations for classification forests.” In 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings, 2014.
Qiu Q, Sapiro G. Learning transformations for classification forests. In: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings. 2014.
Qiu, Q., and G. Sapiro. “Learning transformations for classification forests.” 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings, 2014.
Qiu Q, Sapiro G. Learning transformations for classification forests. 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings. 2014.

Published In

2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings

Publication Date

January 1, 2014