@inproceedings{501b34a5c8944e2a8e325eb821b87988,
title = "Scalable Deep k-Subspace Clustering",
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{\^A} 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 = "Deep learning, Scalable, Subspace clustering",
author = "Tong Zhang and Pan Ji and Mehrtash Harandi and Richard Hartley and Ian Reid",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 14th Asian Conference on Computer Vision, ACCV 2018 ; Conference date: 02-12-2018 Through 06-12-2018",
year = "2019",
doi = "10.1007/978-3-030-20873-8_30",
language = "English",
isbn = "9783030208721",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "466--481",
editor = "Hongdong Li and Konrad Schindler and Greg Mori and C.V. Jawahar",
booktitle = "Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers",
address = "Germany",
}