Outlier-Robust Convex Segmentation
DOI:
https://doi.org/10.1609/aaai.v29i1.9637Keywords:
Segmentation, Unsupervised learning, Outlier robustness, Time seriesAbstract
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.
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Published
2015-02-21
How to Cite
Katz, I., & Crammer, K. (2015). Outlier-Robust Convex Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9637
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Section
Main Track: Novel Machine Learning Algorithms