Computer Science > Computer Vision and Pattern Recognition
[Submitted on 19 Dec 2013 (v1), last revised 6 Feb 2014 (this version, v2)]
Title:Learning Transformations for Classification Forests
View PDFAbstract: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.
Submission history
From: Qiang Qiu [view email][v1] Thu, 19 Dec 2013 16:01:41 UTC (1,658 KB)
[v2] Thu, 6 Feb 2014 12:24:54 UTC (1,658 KB)
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