Outlier-Robust Convex Segmentation
This work addresses outlier-robust segmentation for sequential data, such as speech, but appears incremental as it builds on existing convex optimization methods with specific robustness enhancements.
The authors tackled the problem of segmenting sequential data with outliers by deriving a convex optimization problem and proposing two algorithms, including a novel top-down approach, which outperformed baseline methods on speech segmentation tasks.
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.