The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the "quintessential" observations that best represent clusters in a dataset, by performing joint inference on cluster labels, prototypes and important features. Simultaneously, BCM pursues sparsity by learning subspaces, the sets of features that play important roles in the characterization of the prototypes. The prototype and subspace representation provides quantitative benefits in interpretability while preserving classification accuracy. Human subject experiments verify statistically significant improvements to participants' understanding when using explanations produced by BCM, compared to those given by prior art.
This paper proposes the Latent Case Model (LCM), a Bayesian      approach to clustering in which clusters are represented by a      prototype (a specific sample from the data) and feature      subspaces (a binary subset of the variables signifying those      features that are relevant to the class).  The approach is      presented as being a Bayesian, trainable version of the      Case-Based Reasoning approach popular in AI, and is motivated by      the ways such models have proved highly effective in explaining      human decision making.  The generative model (Figure 1)      represents each item as coming from a mixture of S clusters,      where each cluster is represented by a prototype and subspace      (as above) and a function \phi which generates features matching      those of the prototype with high probability for features in the      subspace, and uniform features outside it. The model is thus      similar in functionality to LDA but quite different in terms of      its representation. Experimental results are shown for      "prediction accuracy" (more on this later) and unsupervised      accuracy of the clusters vs. a baseline of LDA (Figure 2), and      the results seem superior/comparable to LDA.  Further      experiments are shown regarding the interpretability of the      models - human subjects are asked to categorize prototypes from      LCM vs. lists of top features from LDA; the LCM condition      significantly outperformed LDA (85.9% vs. 71.3%).      I greatly enjoyed this paper - not only does it present a novel      method that truly stands out in the vast sea of conventional      LDA-like models, it is refreshingly well-written - the      motivation and background were compelling, the description of      the model and its elements (Section 3) was frankly among the      best I've ever seen, the motivating example (section 3.1) was      clear and extremely helpful, and the experiments were      illustrative (though lacked clarity in places, more details      below). Interpretable models are extremely important, especially      for unsupervised tasks such as clustering - I expect that many      in the NIPS community will find this work compelling and may      want to try this model or variants in their own work.      That said, I do have a few issues, and they have mostly to do      with clarity in the experiments section.  First, in Figure 2a,      I'm a bit confused as to what is going on - the authors are      using unsupervised models on digit/newsgroup data, and then      according to the text are training an SVM based on the labels      from those clusters?  To what end? And then what is that SVM      being used to classify - some held out data?  This is really      unclear, at least to me - the authors do cite [6], which likely      explains it, but really the experimental procedure here should      be fully explained.      The second set of issues have to do with the user study.  While      I applaud the authors for having done this study and properly      randomizing orders, conditions, etc., I had a minor concern.  On      line 361, when describing the target categories for the task,      the authors say "For LCM, we simply 'presented the prototype'" -      what does that mean - were subjects shown the recipe in its      entirety (all ingredients) or just the *name* of the recipe      (i.e., "fudge brownie")?  This needs to be clarified in the      text.  Also, if it is the latter (as I suspect, as in Figure 4      the 'Prototype' column is the recipe name), it seems somewhat      unfair to show only a list of top features (ingredients) for the      baseline (LDA) condition - instead they could show a "prototype"      for each cluster there as well by simply selecting the item      that had the highest posterior for that class (this is a common      heuristic to get a representative sample from an LDA topic).      This is not a huge deal, and likely there wouldn't be time to      run the study again, but this should at least be mentioned in      the discussion.      In any case, the issues above are quite minor; I think this is a      significant and well-written paper that would get a good deal of      positive attention at this year's conference.