Convergence of iteratively re-weighted least squares to robust M-estimators**
This paper presents a way of using the Iteratively Reweighted Least Squares (IRLS) method to minimize several robust cost functions such as the Huber function, the Cauchy function and others. It is known that IRLS (otherwise known as Weiszfeld) techniques are generally more robust to outliers than the corresponding least squares methods, but the full range of robust M-estimators that are amenable to IRLS has not been investigated. In this paper we address this question and show that IRLS...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015|
|01_Aftab_Convergence_of_iteratively_2015.pdf||530.72 kB||Adobe PDF||Request a copy|
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