Sparse Probability Regression by Label Partitioning
Abstract
A large-margin learning machine for sparse probability regression is presented. Unlike support vector machines and other forms of kernel machines, nonlinear features are obtained by transforming labels into higher-dimensional label space rather than transforming data vectors into feature space. Linear multi-class logistic regression with partitioned classes of labels yields a nonlinear classifier in the original labels. With a linear kernel in data space, storage and run-time requirements are reduced from the number of support vectors to the number of partitioned labels. Using the partitioning property of KL-divergence in label space, an iterative alignment procedure produces sparse training coefficients. Experiments show that label partitioning is effective in modeling non-linear decision boundaries with same, and in some cases superior, generalization performance to Support Vector Machines with significantly reduced memory and run-time requirements.
Cite
Text
Chakrabartty et al. "Sparse Probability Regression by Label Partitioning." Annual Conference on Computational Learning Theory, 2003. doi:10.1007/978-3-540-45167-9_18Markdown
[Chakrabartty et al. "Sparse Probability Regression by Label Partitioning." Annual Conference on Computational Learning Theory, 2003.](https://mlanthology.org/colt/2003/chakrabartty2003colt-sparse/) doi:10.1007/978-3-540-45167-9_18BibTeX
@inproceedings{chakrabartty2003colt-sparse,
title = {{Sparse Probability Regression by Label Partitioning}},
author = {Chakrabartty, Shantanu and Cauwenberghs, Gert and Jayadeva, },
booktitle = {Annual Conference on Computational Learning Theory},
year = {2003},
pages = {231-242},
doi = {10.1007/978-3-540-45167-9_18},
url = {https://mlanthology.org/colt/2003/chakrabartty2003colt-sparse/}
}