I'm far from an expert on LOESS (in fact, I hadn't heard the term before now), but it looks like it doesn't perform a comparable function to MIC. LOESS seems to be an algorithm for producing a non-linear regression while MIC is an algorithm to measure the strength of a relationship between two variables.

In the paper (figure 2A), they compare it to Pearson correlation coefficient, Spearman rank correlation, mutual information, CorGC, and maximal correlation on data in a variety of shapes. Basically, it is effective on a wider range of shapes than any of them.

Check out figures S5.D and S6 from the SOM. If the relationship is functional (the linear, parabolic, sinusoidal cases on Figure S6), then the R2 calculated from LOESS regression is quite close to this MIC score, and that's not a coincidence. Of course LOESS R2 just dies when it encounters a non-functional relationship.

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by curiousepic 1 min read17th Dec 20115 comments


I was waiting for someone more knowledgeable to post something about this, but it's been a couple days and thought I'd bring it to LW's attention.

The maximal information coefficient (MIC) is a measure of two-variable dependence developed with the guidelines of generality and equitability in mind. The published paper describing MIC shows that it comes very close to achieving both goals simultaneously, and that it significantly outperforms competing methods in this regard.

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