Scalable Deep k-Subspace Clustering

Publication Date: 12/2/2018

Event: ACCV 2018, Perth, Australia

Reference: pp 466-481, 2019

Authors: Tong Zhang, Australian National University; Pan Ji, NEC Labs America and University of Adelaide; Mehrtash Harandi, Australian National University, Data61-CSIRO and Monash University; Richard Hartley, Australian National University; Ian Reid, University of Adelaide

Abstract: Subspace clustering algorithms are notorious for their scalability issues because building and processing large affinity matrices are demanding. In this paper, we introduce a method that simultaneously learns an embedding space along subspaces within it to minimize a notion of reconstruction error, thus addressing the problem of subspace clustering in an end-to-end learning paradigm. To achieve our goal, we propose a scheme to update subspaces within a deep neural network. This in turn frees us from the need of having an affinity matrix to perform clustering. Unlike previous attempts, our method can easily scale up to large datasets, making it unique in the context of unsupervised learning with deep architectures. Our experiments show that our method significantly improves the clustering accuracy while enjoying cheaper memory footprints.

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