2013/12/19 by Qiang Qiu, Guillermo Sapiro, Qiu, Qiang +1
Computer Science · Mathematics · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Neural Networks and Applications #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1312.5604
arXiv admin note: text overlap with arXiv:1309.2074
openalex publication_date 2013/12/19 · arxiv created 2014/02/06 · arxiv updated 2014/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.