Interpretable Deep Clustering
Jonathan Svirsky Ofir Lindenbaum
Clustering is a fundamental learning task widely used as a first step in data
analysis. For example, biologists often use cluster assignments to analyze
genome sequences, medical records, or images. Since downstream analysis is
typically performed at the cluster level, practitioners seek reliable and
interpretable clustering models. We propose a new deep-learning framework that
predicts interpretable cluster assignments at the instance and cluster levels.
First, we present a self-supervised procedure to identify a subset of
informative features from each data point. Then, we design a model that
predicts cluster assignments and a gate matrix that leads to cluster-level
feature selection. We show that the proposed method can reliably predict
cluster assignments using synthetic and real data. Furthermore, we verify that
our model leads to interpretable results at a sample and cluster level.