RANSAC Regression System

A RANSAC Regression System is a Robustness Regression System that implements a RANSAC Algorithm to solve a RANSAC Regression Task.

• AKA: RANdom SAmple Consensus System, RANSAC Regressor, RANSAC Regression Estimator, RANSAC Estimator.
• Example(s):
  • sklearn.linear_model.RANSACRegressor [1]:
    • Robust linear model estimation using RANSAC
    • Robust linear estimator fitting
• Counter-Example(s):
  • Theil-Sen Regression System.
  • Huber Regression System.
  • LASSO Regression System.
  • Ridge Regression System.
• See: Regression Analysis Task, Random Variable, L2-norm.

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References

2017

• (Scikit Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/linear_model.html#ransac-random-sample-consensus Retrieved:2017-09-17
  • QUOTE: RANSAC (RANdom SAmple Consensus) fits a model from random subsets of inliers from the complete data set.

     RANSAC is a non-deterministic algorithm producing only a reasonable result with a certain probability, which is dependent on the number of iterations (see max_trials parameter). It is typically used for linear and non-linear regression problems and is especially popular in the fields of photogrammetric computer vision.

    The algorithm splits the complete input sample data into a set of inliers, which may be subject to noise, and outliers, which are e.g. caused by erroneous measurements or invalid hypotheses about the data. The resulting model is then estimated only from the determined inliers.

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