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I'm having a little hard time understanding this specific feature selection algorithm.

Specifically, I am looking into maximum-relevance-minimum-redundancy method for feature selection.

If I have a feature matrix $\ X = \{x_1, x_2, ... , x_n\} , x_i \in R^D $

then I can compute mutual information between two specific individual features, but when talking about

relevance of that specific feature to a "class", in terms of mRMR algorithm,

what is exactly the definition of "class" ?

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    $\begingroup$ If you look at Tom Cover's Information Theory book the result will be clear. $\endgroup$
    – Michael R. Chernick
    Commented Mar 8, 2017 at 0:40

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With no knowledge about this particular algorithm, I would guess that the word "class" is being used in the usual way in machine learning, which means "level of a discrete dependent variable". For example, if you're trying to build a classifier that distinguishes men and women, then there are two classes, men and women.

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  • $\begingroup$ But the mRMR algorithm requires that I find the mutual information of a feature to that "class", and I'm not so sure how I could calculate that. but, as Michael said, I will take a look at Tom Cover's book about information theory $\endgroup$
    – Kevvy Kim
    Commented Mar 8, 2017 at 21:50

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