Model-wise and point-wise random sample consensus for robust regression and outlier detection

Popular regression techniques often suffer at the presence of data outliers. Most previous efforts to solve this problem have focused on using an estimation algorithm that minimizes a robust M-estimator based error criterion instead of the usual non-robust mean squared error. However the robustness gained from M-estimators is still low. This paper addresses robust regression and outlier detection in a random sample consensus (RANSAC) framework. It studies the classical RANSAC framework and highlights its model-wise nature for processing the data. Furthermore, it introduces for the first time a point-wise strategy of RANSAC. New estimation algorithms are developed following both the model-wise and point-wise RANSAC concepts. The proposed algorithms’ theoretical robustness and breakdown points are investigated in a novel probabilistic setting. While the proposed concepts and algorithms are generic and general enough to adopt many regression machineries, the paper focuses on multilayered feed-forward neural networks in solving regression problems. The algorithms are evaluated on synthetic and real data, contaminated with high degrees of outliers, and compared to existing neural network training algorithms. Furthermore, to improve the time performance, parallel implementations of the two algorithms are developed and assessed to utilize the multiple CPU cores available on nowadays computers.