Robust Multiple Model Fitting with Preference Analysis and Low-rank Approximation.

This paper deals with the extraction of multiple models from outlier-contaminated data. The method we present is based on preference analysis and low rank approximation. After representing points in a conceptual space, Robust PCA (Principal Component Analysis) and Symmetric NMF (Non negative Matrix Factorization) are employed to reduce the multi-model fitting problem to many single-fitting problems, which in turn are solved with a strategy that resembles MSAC (M-estimator SAmple Consensus). Experimental validation on public, real data-sets demonstrates that our method compares favourably with the state of the art.