Interpretable Subspace Clustering

Subspace clustering is one of the most popular clustering methods due to its effectiveness. Although subspace clustering methods have been demonstrated to achieve promising performance, they still lack interpretability, especially when handling high-dimensional complicated data. To bridge this gap, this paper focuses on the interpretability of subspace clustering and proposes a novel interpretable subspace clustering method. Our goal is to answer two key questions about the interpretability in subspace clustering: 1) when handling an individual sample, which features should work for this sample? 2) Which cluster or subspace will the features that work put this sample into? To answer these two questions, we design two new interpretability regularized terms and plug them into the subspace clustering. In this way, we show that interpretability can be used to improve the clustering performance in turn. Extensive experiments on benchmark data sets demonstrate the effectiveness of our method in terms of clustering performance and interpretability.