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|Title:||Scalable deep k-subspace clustering|
|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|
|Series/Report no.:||Lecture Notes in Computer Science ; 11365|
|Conference Name:||Asian Conference on Computer Vision (ACCV) (02 Dec 2018 - 06 Dec 2018 : Perth, Australia)|
|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|
|Appears in Collections:||Computer Science publications|
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