Fitting noisy data with the RANSAC algorithm
We discussed the issue of outliers in the context of regression elsewhere in this book (refer to the See also section at the end of this recipe). The issue is clear—the outliers make it difficult to properly fit our models. The RANdom SAmple Consensus algorithm (RANSAC) does a best effort attempt to fit our data in an iterative manner. RANSAC was introduced by Fishler and Bolles in 1981.
We often have some knowledge about our data, for instance the data may follow a normal distribution. Or, the data may be a mix produced by multiple processes with different characteristics. We could also have abnormal data due to glitches or errors in data transformation. In such cases, it should be easy to identify ...
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