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The new criterion denoted as the Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR) is constructed from two essential tools and the robustness of the criterion is given by robustness of both tools.
It is called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR).
We introduce a new version of the MRMR criterion using a regularized redundancy measure of Section 3.1 and a robust relevance measure of Section 3.2.
It is denoted as Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR) and can be interpreted as insensitive to the presence of outliers in the continuous measurements X.
We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups.
MRRMRR performs as the most robust approach among other variable selection procedures, while the choice of the weights for the robust relevance measure seems to play a negligible role.
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