5/3/2023 0 Comments Mahalanobis distance opencv![]() Therefore we can use PCA as a stepping stone for outliers detection in classification. The PLS-based method is great when you have the primary reference values associated with your spectra (the “labels”), but can’t be used for unlabelled data.Ĭonversely, Principal Components Analysis (PCA) can be used also on unlabelled data – it’s very useful for classification problems or exploratory analysis. What we are going to work out today is instead a (simpler) method, very useful for classification problems. If you want to refresh your memory read this post: Outliers detection with PLS. The aficionados of this blog may remember that we already discussed a (fairly involved) method to detect outliers using Partial Least Squares. Today we are going to discuss one of these good methods, namely the Mahalanobis distance for outlier detection. We can however work out a few good methods to help us make sensible judgements. How do we know a data point is an outlier? How do we make sure we are detecting and discarding only true outliers and not cherry-picking from the data? Well, all of these are rhetorical questions, and we can’t obviously give a general answer to them. ![]() Detecting outliers in a set of data is always a tricky business. ![]()
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