MRMR, known as Minimum Redundancy Maximum Relevance, however, attempts to detect those redundant subsets, find them out, and delete them.
So, a method based on the Minimum Redundancy Linear Array was then adopted.
The sequential forward selection, genetic and maximum relevance minimum redundancy algorithms are used for a precise selection of features.
Although it has been known for some time that the minimum redundancy for IBI-free designs of block transceivers is ⌈L/2⌉, only recently practical DFT-based solutions using minimum redundancy were proposed.
Then the features are carefully selected by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) methods.
Minimum redundancy array.
Before classification, the minimum redundancy Maximum Relevance (mRMR) feature selection method was first applied to choose subsets of useful features.
We considered that each encoder has an extra output (minimum redundancy).
Minimum redundancy arrays (MRAs) and minimum hole arrays (MHAs) are two common classes of nonuniform linear arrays [6 9].
The first term in the equation is the maximum relevance condition, and the second term is the minimum redundancy condition.
In this study, we use maximum relevance minimum redundancy (MRMR), which is a frequently used feature selection algorithm.
