Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection. By placing interpretability criteria directly into the model, we allow for the model to both optimize parameters related to interpretability and to directly report a global set of distinguishable dimensions to assist with further data exploration and hypothesis generation. MGM extracts distinguishing features on real-world datasets of animal features, recipes ingredients, and disease co-occurrence. It also maintains or improves performance when compared to related approaches. We perform a user study with domain experts to show the MGM's ability to help with dataset exploration.
(Light Review) This paper presents a clustering method that     purports to provide greater interpretability for what     distinguishes one cluster from another.The authors do this by     first providing a feature selection method that creates logical     rules to group sets of features that vary together, and second     by modeling distinguishing parameters as having a bi-modal     distribution (i.e., with a gap in feature values that     distinguishes different clusters) vs. non-distinguishing     parameters as having a unimodal distribution (possibly of high     variance).These choices are well motivated and the development     of the technique is relatively clear; the work also appears to     be novel in its approach.Results are shown first in terms of     mutual information with underlying labeled clusters, for which     it performs in the top two for the set of baselines presented in     both data scenarios.However, the main point of the technique     (and the paper) was greater interpretabilty, and the evaluation     for this is very weak - the authors essentially talk about the     features that were found as distinguishing and explain that     they're reasonable.While their explanations are sensible,     they're ultimately insufficient to justify the premise of the     paper, as (a) such a subjective judgment requires multiple     judgments by human subjects (via Turk, etc.) rather than     arguments by the authors, and (b) no baseline for comparison is     shown, while in fact there are other methods that purport to     describe the differences between clusters (albeit focused on the     most discriminating features, as the authors point out).Since     this was presented as the primary contribution of the paper, it     seems that the authors have not sufficiently demonstrated that     their method has satisfied this claim.