Cascade Subspace Clustering

• DOI: 10.1609/aaai.v31i1.10824
• Publication Date: 2022-06-24T17:12:22Z
• Authors: Xi Peng; Jiashi Feng; Jiwen Lu; Wei-Yun Yau; Zhang Yi
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

In this paper, we recast the subspace clustering as a verification problem. Our idea comes from an assumption that the distribution between a given sample x and cluster centers Omega is invariant to different distance metrics on the manifold, where each distribution is defined as a probability map (i.e. soft-assignment) between x and Omega. To verify this so-called invariance of distribution, we propose a deep learning based subspace clustering method which simultaneously learns a compact representation using a neural network and a clustering assignment by minimizing the discrepancy between pair-wise sample-centers distributions. To the best of our knowledge, this is the first work to reformulate clustering as a verification problem. Moreover, the proposed method is also one of the first several cascade clustering models which jointly learn representation and clustering in end-to-end manner. Extensive experimental results show the effectiveness of our algorithm comparing with 11 state-of-the-art clustering approaches on four data sets regarding to four evaluation metrics.