Outlier-Robust Convex Segmentation
Machine Learning
2014-11-19 v2 Machine Learning
Abstract
We derive a convex optimization problem for the task of segmenting sequential data, which explicitly treats presence of outliers. We describe two algorithms for solving this problem, one exact and one a top-down novel approach, and we derive a consistency results for the case of two segments and no outliers. Robustness to outliers is evaluated on two real-world tasks related to speech segmentation. Our algorithms outperform baseline segmentation algorithms.
Cite
@article{arxiv.1411.4503,
title = {Outlier-Robust Convex Segmentation},
author = {Itamar Katz and Koby Crammer},
journal= {arXiv preprint arXiv:1411.4503},
year = {2014}
}
Comments
* Accepted to AAAI-15, this version includes the appendix/supplementary material referenced in the AAAI-15 submission, as well as color figures * This version include some minor typos correction