Interpretable Deep Clustering
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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.
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