Answers are grounded in this paper and cite their sources.
This paper presents an improvement of the J-linkage algorithm for fitting multiple instances of a model to noisy data corrupted by outliers. The binary preference analysis implemented by J-linkage is replaced by a continuous (soft, or fuzzy) generalization that proves to perform better than J-linkage on simulated data, and compares favorably with state of the art methods on public domain real datasets.
No takes yet. Share an insight, caveat, or question.
Magri et al. (2014) studied this question.