raedda: raedda: Robust Adaptive Eigenvalue Decomposition Discriminant...
In AndreaCappozzo/raedda: Robust Adaptive Eigenvalue Decomposition Discriminant Analysis
Description
RAEDDA is a robust generalization of the AMDA methodology (Bouveyron, 2014) that
accounts for outliers and label noise detecting observations with the lowest contributions to the overall likelihood employing impartial trimming.
It performs Supervised Learning in Presence of Outliers, Label Noise and Unobserved Classes employing MVN mixture model with Parsimonious structure.
Parameters estimation is carried out by either considering a transductive or an inductive approach
raedda functions
RAEDDA_transductive estimates parameters employing a transductive (semi-supervised) approach
AndreaCappozzo/raedda documentation built on July 21, 2021, 10:48 a.m.
R Package Documentation
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Description
RAEDDA is a robust generalization of the AMDA methodology (Bouveyron, 2014) that accounts for outliers and label noise detecting observations with the lowest contributions to the overall likelihood employing impartial trimming. It performs Supervised Learning in Presence of Outliers, Label Noise and Unobserved Classes employing MVN mixture model with Parsimonious structure. Parameters estimation is carried out by either considering a transductive or an inductive approach
raedda functions
RAEDDA_transductive estimates parameters employing a transductive (semi-supervised) approach
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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