The most important link is the Supplementary Material. I only found it through the reddit thread. (Not much else to go there for. Maybe the pirated paper itself, but that is basically just an extended abstract of the SOM.) Here is the link to the SOM:

Figures S5 and S6 (page 41) make me conjecture that compared to LOESS, this new method is an improvement only when the relationship is not a function (but a many-valued function). Not that I am really familiar with LOESS.

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.

MINE: Free tool for detecting novel associations in large data sets

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.

Video summary

Main site with access to tool