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Type: Conference paper
Title: Scalable deep k-subspace clustering
Author: Zhang, T.
Ji, P.
Harandi, M.
Hartley, R.
Reid, I.
Citation: Proceedings of the 14th Asian Conference on Computer Vision (ACCV 2018), as published in Lecture Notes in Computer Science, 2019 / vol.11365, pp.466-481
Publisher: Springer
Publisher Place: Switzerland
Issue Date: 2019
Series/Report no.: Lecture Notes in Computer Science ; 11365
ISBN: 9783030208721
ISSN: 0302-9743
Conference Name: Asian Conference on Computer Vision (ACCV) (02 Dec 2018 - 06 Dec 2018 : Perth, Australia)
Statement of
Tong Zhang, Pan Ji, Mehrtash Harandi, Richard Hartley, and Ian Reid
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.
Keywords: Subspace clustering; Deep learning; Scalable
Rights: © Springer Nature Switzerland AG 2019
RMID: 0030118123
DOI: 10.1007/978-3-030-20873-8_30
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Appears in Collections:Computer Science publications

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