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Outlier-resilient model fitting via percentile losses: Methods for general and convex residuals
- Publication Year :
- 2024
-
Abstract
- We consider the problem of robustly fitting a model to data that includes outliers by formulating a percentile optimization problem. This problem is non-smooth and non-convex, hence hard to solve. We derive properties that the minimizers of such problems must satisfy. These properties lead to methods that solve the percentile formulation both for general residuals and for convex residuals. The methods fit the model to subsets of the data, and then extract the solution of the percentile formulation from these partial fits. As illustrative simulations show, such methods endure higher outlier percentages, when compared with standard robust estimates. Additionally, the derived properties provide a broader and alternative theoretical validation for existing robust methods, whose validity was previously limited to specific forms of the residuals.<br />Comment: 5 pages, 1 figure. Submitted to IEEE Signal Processing Letters on 21-Apr-2024
- Subjects :
- Electrical Engineering and Systems Science - Signal Processing
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2405.09436
- Document Type :
- Working Paper